Ai Ml Dev in India

Opportunity Overview: We are seeking a Senior Software Engineer - AI to join our Engineering team. In this role, you will build the intelligent agents and applications serving health insurance plans covering over 15 million people. You'll partner closely with product, data science, and clinical teams to build the foundation that transforms how clinical intelligence is delivered at scale. This is an opportunity to make a direct impact on healthcare outcomes while working with modern technologies in a fast-paced, collaborative environment. What you’ll do: Agent Development : Participate in the development, evaluation, and deployment of Cohere’s AI-powered agents and applications. Data & Retrieval Architecture : Design data and retrieval architectures that give AI agents the right context across diverse healthcare sources. Hands-On Engineering : Design, review, and own high-quality agentic code — leading releases, production deployments and on-call support. Cross-Functional Collaboration : Work closely with ML/DS teams, product teams, and architects to understand requirements and ensure systems meet business needs. Security & Compliance : Ensure all agentic components comply with healthcare security and privacy regulations (e.g., HIPAA) and adhere to industry best practices for security. Agile Development : Contribute to sprint planning, execution, and retrospectives, driving efficiency and velocity within the engineering team and collaborating closely with stakeholders to align with product goals and business priorities. ISMS roles and responsibilities: Good knowledge of Information practices. Assist the manager in all the information security activities implementation and maintenance process. Ensuring the team and imparted with Competence related to Information security Responsible for implementation of security policies and procedures and report any issues to the Information Security Manager. Required Qualifications: Must-haves Bachelor’s degree

We are expanding our agentic AI capability and are looking for an AI Engineer to join the team. You will work alongside senior engineers to build and maintain AI systems — contributing to agentic pipelines, retrieval infrastructure, and the integrations that tie these systems together. This is a hands-on implementation role with real ownership of components. You will grow your skills in a fast-moving AI practice, working on production systems that directly affect client outcomes. What This Involves: Build and maintain agentic pipelines and workflows under the guidance of senior engineers: tool use, orchestration, and multi-step reasoning. Implement and tune RAG pipelines — including embedding, chunking strategies, vector retrieval, and retrieval evaluation. Contribute to memory and context layer components: integrating vector databases, supporting knowledge graph pipelines, and helping maintain state management across agentic systems. Write clean, well-tested Python code and participate in code reviews. Debug and improve existing AI systems based on evaluation results and production feedback. Collaborate with data engineers and domain experts to integrate AI components with upstream data sources and downstream applications. Document implementations clearly and contribute to shared internal tooling. Requirements: 2–4 years of software or ML engineering experience, with at least 1 year working with LLMs or AI systems in a professional setting. Working knowledge of LLM APIs (OpenAI, Anthropic, or similar) and at least one agentic or RAG framework (LangChain, LlamaIndex, or equivalent). Solid Python skills and comfort with software engineering basics: version control, testing, REST APIs. Familiarity with vector databases or embedding-based search. Curiosity about agentic AI — you follow developments in the space and are eager to apply new techniques. Excellent communication and collaboration skills — comfortable working across cross-functional and client-facing te

About Ema Ema is building the world’s leading Agentic AI platform to transform enterprise productivity. We enable organizations to delegate repetitive tasks to Ema, the Universal AI Employee, delivering 10x gains in workforce efficiency, across functions. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs. We are backed by industry leading investors including Accel, Naspers/Prosus, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver and Bangalore, Ema is at the frontier of what Agentic AI can do in production — we ship real systems that run real business processes at scale. We are looking for an experienced Tech Recruiter (5+ years) to drive end-to-end hiring for technical roles across engineering and product teams. You will partner closely with hiring managers, founders/leadership, and cross-functional stakeholders to build strong talent pipelines, improve hiring processes, and deliver a high-quality candidate experience. This is a high-impact role requiring strong stakeholder management, sourcing expertise, and the ability to hire at speed without compromising quality. Key Responsibilities End-to-End Tech Hiring Own the full recruitment lifecycle: intake, sourcing, screening, interview coordination, offer management, and onboarding support. Manage multiple requisitions across levels (IC, lead, manager) and functions (Backend, Frontend, Full Stack, Mobile, Data, ML, DevOps, QA). Run structured intake meetings and align hiring plans with stakeholders. Sourcing & Pipeline Development Build proactive talent pipelines using LinkedIn Recruiter, GitHub, job boards, communities, and referrals. Execute creative sourcing strategies for niche and high-demand technical skills. Maintain consistent pipeline health and share weekly hiring progres

Role Purpose: At Jumio, you will work for one of the market leaders in the global identity verification space that is helping to make the digital world a safer place for everyone. As a Software Development Engineer in the MLOpsTeam, you will develop the blueprint for highly scalable and performant ML model serving. Role Value: As a Software Engineer (SDE III), you will drive the continuous improvement of the infrastructure and applications to manage the lifecycle of ML assets (data, models) to better developer experience and strengthen governance capabilities. Secondly, you will design and implement robust ML infrastructure for model deployment, serving, and optimization. You will work on efficient CI/CD pipelines for ML models and leverage advanced compilers or hardware optimization to maximize inference performance while optimizing costs. We welcome you to challenge us to impact our software development processes and tools. Example Responsibilities: Upgrade ML assets (models, data) management systems for better developer experience and robust governance capabilities Build and optimize model serving infrastructure with a focus on inference latency and cost optimization Architect efficient inference pipelines that balance latency, throughput, and cost across various acceleration options Implement cost-efficient, enterprise-scale solutions Collaborate in a cross-functional, distributed team for continuous system improvement Work with MLEs, QA Engineers, and DevOps Engineers Evaluate and implement new technologies and tools Contribute to architectural decisions for distributed ML systems Experience and Qualifications : 5+ years of experience in software engineering with Python Experience with model lifecycle management (MLFlow, Weights & Biases or equivalent) Experience with data management ecosystem (quality, transformation, catalog) Experience with ML frameworks, particularly PyTorch Experience optimizing ML models with hardwar

About Ema Ema is building the world’s leading Agentic AI platform to transform enterprise productivity. We enable organizations to delegate repetitive tasks to Ema, the Universal AI Employee, delivering 10x gains in workforce efficiency, across functions. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs. We are backed by industry leading investors including Accel, Naspers/Prosus, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver, Ema is at the frontier of what Agentic AI can do in production — we ship real systems that run real business processes at scale. Who you are You are an experienced Platform Engineer who owns backend infrastructure end to end. You design multi-tenant, microservices-based systems that other engineering teams build on, and you make deliberate architectural tradeoffs around consistency, latency, scale, and cost. You are comfortable going deep — service mesh internals, database internals, distributed-systems failure modes — and equally comfortable defining the reliability and security contracts an enterprise AI platform depends on. Responsibilities Design, own, and evolve scalable microservices architectures on Kubernetes across GCP, Azure, and AWS, including multi-tenant isolation (namespaces, network policies, per-tenant resource quotas and RBAC). Build core platform and data-plane components in Golang and Python — data ingestion, knowledge-base indexing and vector/graph search, application connectivity, workflow automation, and ML operations — against explicit latency and throughput SLOs. Own service-to-service communication: gRPC/protobuf API contracts, service mesh (Istio/Linkerd), load balancing, retries, timeouts, and circuit breaking. Make and document architectural tradeoffs — partitioning/sharding strat

About Ema Ema is building the world’s leading Agentic AI platform to transform enterprise productivity. We enable organizations to delegate repetitive tasks to Ema, the Universal AI Employee, delivering 10x gains in workforce efficiency, across functions. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs. We are backed by industry leading investors including Accel, Naspers/Prosus, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver, Ema is at the frontier of what Agentic AI can do in production — we ship real systems that run real business processes at scale. Who you are You are an experienced Infrastructure Engineer Engineer who owns backend infrastructure end to end. You design multi-tenant, microservices-based systems that other engineering teams build on, and you make deliberate architectural tradeoffs around consistency, latency, scale, and cost. You are comfortable going deep — service mesh internals, database internals, distributed-systems failure modes — and equally comfortable defining the reliability and security contracts an enterprise AI platform depends on. Responsibilities Design, own, and evolve scalable microservices architectures on Kubernetes across GCP, Azure, and AWS, including multi-tenant isolation (namespaces, network policies, per-tenant resource quotas and RBAC). Build core platform and data-plane components in Golang and Python — data ingestion, knowledge-base indexing and vector/graph search, application connectivity, workflow automation, and ML operations — against explicit latency and throughput SLOs. Own service-to-service communication: gRPC/protobuf API contracts, service mesh (Istio/Linkerd), load balancing, retries, timeouts, and circuit breaking. Make and document architectural tradeoffs — partitioning

TEGNA Inc. helps people thrive in their local communities by providing the trusted local news and services that matter most. With 64 television stations in 51 U.S. markets, TEGNA reaches more than 100 million people monthly across web, mobile apps, streaming, and linear television, while also maintaining a strong global presence in India with offices in Bangalore and Chennai that support technology, product, and business operations initiatives. Together, we are building a sustainable future for local news. Position Overview TEGNA is looking for a Senior Data Analyst is seeking a skilled and forward-thinking CloudEngineer to join our growing technology team. We are a demand-side platform (DSP) helping advertisers and agencies programmatically reach their target audiences at scale. We are looking for a Senior Data Analyst to drive insights across advertiser performance, audience quality, and supply-side partnerships — going beyond surface-level reporting to uncover the stories that data tells. In this role, you will work at the intersection of data, engineering, and data science — collaborating closely with our engineering team to maintain data integrity and with our ML team to translate model outputs into business intelligence. You will analyse how audiences behave across our platform, evaluate the quality and efficiency of inventory sources, develop forecasts that help advertisers and internal teams plan with confidence, and surface actionable intelligence that helps our advertisers deliver better outcomes while growing the overall business. What You’ll Do • Analyse audience behaviour and segment performance to help advertisers improve targeting efficiency, reach, and campaign ROI across programmatic channels. • Evaluate supply-side inventory quality — assessing publisher segments, bid stream data, win rates, and CPM trends — to inform smarter buying decisions and supply curation strategi

Role: DataOps Solution Architect — Azure Data Platform (Fabric & Databricks) Owns end-to-end architecture of the Azure data platform (Fabric, Databricks, Azure services) and sets the governance, security, and DataOps standards engineering teams implement. Design authority and primary architecture advisor to the client, not the day-to-day builder. Experience Required 8-10+ years overall in data engineering / cloud architecture, including 3+ years as a Solution Architect or in a comparable design-authority role. Microsoft Fabric: 2+ years hands-on. Azure Databricks: 4+ years hands-on. Proven track record designing and delivering enterprise-scale Azure data platforms end-to-end. Azure is mandatory ; experience across multiple client engagements or a consulting/SI background is a plus. Core Technical Expertise Azure Platform Fabric & Databricks architecture. ADF. ADLS. Entra ID. Key Vault. Monitor/Log Analytics. Microsoft Fabric Workspace & environment strategy. Lakehouse/Warehouse. OneLake. Pipelines/Notebooks. REST APIs/CLI. Governance. Azure Databricks Workspace strategy. Unity Catalog. RBAC. Jobs/Workflows. Delta Lake. Asset Bundles. ML platform integration. DevOps & IaC Azure DevOps/GitHub CI/CD. Terraform & Bicep. Branching/release strategy. Reusable IaC modules. Security & Networking Entra ID. RBAC architecture. Private Endpoints/DNS. Key Vault integration. DataOps Standards CI/CD & test gates for pipelines. Data-quality frameworks. Observability/alerting architecture. SLAs/SLOs. Incident-management practices. FinOps guardrails. Key Responsibilities Own end-to-end architecture; translate requirements into scalable, secure, cost-effective designs. Design enterprise-scale Fabric/Databricks architecture including workspace, networking, security, and governance. Set standards for CI/CD, IaC, DataOps, and environment management; ensure enterprise security and regulatory compliance. Evaluate technology options; identify architectural risks a

Synthesia is the world’s leading AI video platform for business, used by over 90% of the Fortune 100. Founded in 2017, the company is headquartered in London, with offices and teams across Europe and the US. As AI continues to shape the way we live and work, Synthesia develops products to enhance visual communication and enterprise skill development, helping people work better and stay at the center of successful organizations. Following our recent Series E funding round, where we raised $200 million, our valuation stands at $4 billion. Our total funding exceeds $530 million from premier investors including Accel, NVentures (Nvidia's VC arm), Kleiner Perkins, GV, and Evantic Capital, alongside the founders and operators of Stripe, Datadog, Miro, and Webflow. What you'll do at Synthesia As a Research Engineer you will join a team of 40+ Researchers and Engineers within the R&D Department working on cutting-edge challenges in the Generative AI space, with a focus on creating high-quality, expressive and real-time synthetic voices. Within the team you’ll have the opportunity to work on the applied side of our research efforts and directly impact our solutions that are used worldwide by over 60,000 businesses. If you are an expert in ML, LLMs, speech generation, conversational models , this is your chance to make a global impact. You will join our Audio Post-Training Team , which works on generative speech and voice synthesis , ensuring our in-house voice models reach production-level quality, speed, and robustness. Typical projects include: Develop and evaluate streaming and speech-to-speech systems, enabling low-latency, interactive voice synthesis. Adapt models for new conditioning inputs (emotion, speed, prosody, speaker control, etc.). Implement post-training optimization techniques (quantization, pruning, distillation) to improve efficiency and latency in real-time speech generation. Integrate and test novel architectures, such as neural codecs, diffusion, or

Senior Machine Learning Engineer Description - We are looking for a Senior MLOps Engineer to design, build, and operate the infrastructure that enables machine learning models and large language models to be deployed safely, reliably, and at scale. In this role, you will create the end-to-end capabilities required to move models from experimentation into production, expose them through secure and highly available endpoints, and enable users and applications to interact with AI-powered services. You will work across AWS and Databricks to establish robust CI/CD pipelines, model-serving infrastructure, observability, governance, rollback mechanisms, and operational standards. You will partner closely with data scientists, machine learning engineers, software engineers, security teams, and platform engineers. The ideal candidate combines strong cloud and DevOps engineering skills with a practical understanding of machine learning systems, LLM deployment patterns, and production reliability. Key Responsibilities MLOps Platform and Architecture Design and implement a scalable MLOps platform using AWS and Databricks. Define reference architectures and reusable deployment patterns for traditional machine learning models, deep learning models, and large language models. Build standardized workflows that move models from development and validation into staging and production. Develop self-service capabilities that allow data scientists and ML engineers to deploy models without manually managing infrastructure. Establish clear separation between development, testing, staging, and production environments. Design multi-region or multi-availability-zone architectures where required by business continuity and availability objectives. CI/CD and

Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk. About Okta Okta is an enterprise grade identity management service, built from the ground up in the cloud and delivered with an unwavering focus on customer success. With Okta you can manage access across any application, person, or device. Whether the people are employees, partners, or customers, or the applications are in the cloud, on premises, or on a mobile device, Okta helps you become more secure, make people more productive, and maintain compliance. The Okta service provides directory services, single sign-on, strong authentication, provisioning, workflow, and built in reporting. It runs in the cloud on a secure, reliable, extensively audited platform and integrates deeply with on premises applications, directories, and identity management systems. About the Team The Data Platform team is responsible for the foundational data services, systems, and data products for Okta that benefit our users. Today, the Data Platform team solves challenges and enables: Streaming analytics Interactive end-user reporting Data and ML platform for Okta to scale Telemetry of our products and data Our elite team is fast, creative and flexible. We encourage ownership. We expect great things from our engineers and reward them with stimulating new projects, new technologies and the chance to have significant equity in a company. Okta is about to change the cloud computing landscape forever. About the Position This is an opportunity for experienced Softwa

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. The Customer Experience Engineering Team builds the internal and external technologies that scale Snowflake’s global support and sales organizations. We empower our technical experts by providing the advanced tools they need to resolve complex issues and drive customer success. Our team specializes in software engineering, data-driven decisions, ML, and LLM-based solutions . We build production-grade systems to automate manual processes and augment the capabilities of our technical staff. Our current focus includes: LLMs : Developing and deploying LLM and agent-based architectures for streamlining troubleshooting Scalable Evaluations : Implementing large-scale evaluations to ensure the quality and reliability of our internal and external tools Process Automation : Designing intelligent workflows that eliminate bottlenecks and allow our experts to focus on the most technical aspects of the Snowflake platform Incident discovery: using embeddings, LLMs, clustering, and agents to detect potential widespread issues more quickly Now, the team is growing, and we are looking for a Software Engineer to join us. In this role, you will work closely with the state of the art LLM models, fine-tune them, develop agents, apply various clusterings, summarizations, embeddings, and so on. Ev

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. The Customer Experience Engineering Team builds the internal and external technologies that scale Snowflake’s global support and sales organizations. We empower our technical experts by providing the advanced tools they need to resolve complex issues and drive customer success. Our team specializes in software engineering, data-driven decisions, ML, and LLM-based solutions . We build production-grade systems to automate manual processes and augment the capabilities of our technical staff. Our current focus includes: LLMs : Developing and deploying LLM and agent-based architectures for streamlining troubleshooting Scalable Evaluations : Implementing large-scale evaluations to ensure the quality and reliability of our internal and external tools Process Automation : Designing intelligent workflows that eliminate bottlenecks and allow our experts to focus on the most technical aspects of the Snowflake platform Incident discovery: using embeddings, LLMs, clustering, and agents to detect potential widespread issues more quickly Now, the team is growing, and we are looking for a Software Engineer to join us. In this role, you will work closely with the state of the art LLM models, fine-tune them, develop agents, apply various clusterings, summarizations, embeddings, and so on. Ev

Role Purpose We’re looking for a Staff/Senior Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You’ll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process. What You’ll Do Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition) Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions. Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps. Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS. Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team. What We’re Looking For Experience: 5+ years of industry experience in Machine Learning, with at least 3 years dedicated to Biometrics or Face Analysis. Deep expertise in computer vision and biometrics, especially face recognition. Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact. Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code. Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow. Cloud Native: Hands-on ex

Machine Learning Engineer IV – (Computer Vision) We’re looking for a Staff/Senior Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You’ll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process. What You’ll Do Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition) Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions. Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps. Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS. Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team. What We’re Looking For Strong industry experience in Machine Learning, dedicated to Biometrics or Face Analysis. Deep expertise in computer vision and biometrics, especially face recognition. Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact. Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code. Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow. Cloud Native: Hands-on exper