Enterprise Ai Transformation Lead in India

About Paytm Paytm is a pioneer of digital payments in India, serving over 450 million consumers and 45 million merchants across payments, financial services, and commerce. Over the years, Paytm has built deep in-house capabilities across technology, data, and operations to operate at scale with high reliability. Paytm is building a full stack AI platform focussed on Inference and Agents, enabling large enterprises to deploy AI driven automation across sales, service, operations, and analytics. The Inference and Agentic AI team operates as a cross functional unit spanning engineering, product, data science, business management, and sales, and owns the full lifecycle of AI solutions from opportunity discovery to deployment and scale. Role Overview Paytm is looking to hire a Client Onboarding Director to own implementation delivery and client onboarding governance for Paytm’s AI Inference and Agentic AI products across enterprise clients. This is a managerial role that will lead Client Onboarding Managers and ensure that enterprise deployments move smoothly from sales closure to go live and early adoption. The role sits at the intersection of client teams, product, engineering, business, and sales, and is responsible for converting signed enterprise deals into successful, timely, and scalable deployments. The candidate will own delivery planning, integration governance, risk management, stakeholder communication, and post go live stabilization across enterprise AI agent deployments. The role requires strong program management, technical understanding, client handling, and ability to drive execution across multiple internal and external teams. Key Responsibilities Own end to end delivery governance for enterprise AI agent deployments from sales handoff to go live and stabilization. Lead the implementation planning process across scope, timelines, milestones, dependencies, risks, and success metrics. Ensure every enterprise deployment has a clear project plan, own

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. The Role The Customer Engagement Manager owns a portfolio of strategic enterprise accounts end-to-end: every engagement, every outcome, every relationship, every expansion opportunity. Think of this as the McKinsey Associate Partner model applied to enterprise AI delivery. You are the single point of accountability for your accounts. The customer calls you — not your manager — when they have a problem. You staff delivery teams, oversee solution quality, run executive readouts, measure ROI, drive adoption, rebuild trust when things break, and grow the book through proven production value. This is not a project management role. This is an account ownership role that combines delivery orchestration, outcome ownership, customer leadership, and commercial growth. What You’ll Own 1. Account Ownership Own your accounts end-to-end: every engagement, every outcome, every relationship. Everything good or bad stops with you. Be the single point of contact the customer calls for any problem — delivery, quality, adoption, escalation, or expansion. Maintain a holistic view of each account: engagement status, risk exposure, opportunity pipeline, and the customer’s strategic priorities for the qua

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. Ema is building the world’s first Universal AI Employee — an agentic AI platform that automates complex, cross-system enterprise workflows end-to-end, with humans in the loop where it matters. Unlike copilots or narrow automation tools, Ema deploys production-grade multi-agent systems that integrate deeply with enterprise SaaS platforms and execute real business processes at scale. Our customers don’t experiment — they replace brittle, manual operations with reliable AI systems that deliver measurable outcomes. Founded by leaders from Google, Coinbase, and Okta , and backed by top-tier investors, Ema operates at the frontier of enterprise AI execution , not demos. With teams across Silicon Valley and Bangalore, we are defining how agentic AI is delivered responsibly, reliably, and at scale. If you care about building and shipping real AI systems that work in production , this role is for you. Who You Are The Customer Engagement Manager owns the delivery and stabilization of Ema’s agentic AI solutions — from commitment through production rollout and steady state. This is not a research role , a support role or a coordination only project management role. You are the delivery leader

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

DataHub is an AI & Data Context Platform adopted by over 3,000 enterprises, including Apple, CVS Health, Netflix, and Visa. Innovated jointly with a thriving open-source community of 13,000+ members, DataHub's metadata graph provides in-depth context of AI and data assets with best-in-class scalability and extensibility. The company's enterprise SaaS offering, DataHub Cloud, delivers a fully managed solution with AI-powered discovery, observability, and governance capabilities. Organizations rely on DataHub solutions to accelerate time-to-value from their data investments, ensure AI system reliability, and implement unified governance, enabling AI & data to work together and bring order to data chaos. About the Role We're seeking an experienced DevOps/ Site Reliability Engineering (SRE) Engineer to join DataHub and drive the reliability, scalability, and operational excellence of our platform offerings. In this role, you'll work on technical initiatives across DataHub Cloud and our emerging enterprise deployment solution, which provides customers with enhanced control and flexibility for running DataHub in their preferred environments. Key Responsibilities Enterprise Platform Development: Partner with product and engineering teams to influence the development of advanced deployment capabilities. Collaborate with cross-functional teams to help build systems for seamless installation, upgrade, and rollback processes across various environments. Influence the design and help implement comprehensive monitoring and health check systems for distributed deployments. Partner with engineering teams to help develop self-healing and automated remediation capabilities. Platform Reliability and Operations: Establish and maintain SLAs/SLOs for both cloud and enterprise offerings. Lead incident response and post-mortem processes to drive continuous improvement. Optimise system performance, capacity planning, and cost efficiency. Work closely with product, engineerin

Client Onboarding Director, Inference and Agentic AI Location: Noida Company: Paytm About Paytm Paytm is a pioneer of digital payments in India, serving over 450 million consumers and 45 million merchants across payments, financial services, and commerce. Over the years, Paytm has built deep in-house capabilities across technology, data, and operations to operate at scale with high reliability. Paytm is building a full stack AI platform focussed on Inference and Agents, enabling large enterprises to deploy AI driven automation across sales, service, operations, and analytics. The Inference and Agentic AI team operates as a cross functional unit spanning engineering, product, data science, business management, and sales, and owns the full lifecycle of AI solutions from opportunity discovery to deployment and scale. Role Overview Paytm is looking to hire a Client Onboarding Director to own implementation delivery and client onboarding governance for Paytm’s AI Inference and Agentic AI products across enterprise clients. This is a managerial role that will lead Client Onboarding Managers and ensure that enterprise deployments move smoothly from sales closure to go live and early adoption. The role sits at the intersection of client teams, product, engineering, business, and sales, and is responsible for converting signed enterprise deals into successful, timely, and scalable deployments. The candidate will own delivery planning, integration governance, risk management, stakeholder communication, and post go live stabilization across enterprise AI agent deployments. The role requires strong program management, technical understanding, client handling, and ability to drive execution across multiple internal and external teams. Key Responsibilities Delivery Ownership and Implementation Governance Own end to end delivery governance for enterprise AI agent deployments from sales handoff to go live and stabilization. Lead the implementation planning process across scope

Job Details: Job Description: Intel's Design Quality and Reliability organization is seeking an AI Platform Engineer to architect and build an enterprise-grade AI platform for mission-critical engineering work. This platform will enable Intel engineers to analyze complex design, qualification, and reliability data; automate engineering workflows; access organizational knowledge; and make faster, evidence-based decisions throughout the product lifecycle. The successful candidate will combine strong software engineering fundamentals with expertise in AI-native and agentic development. They will be highly proficient with Agentic AI coding assistants and able to use these tools responsibly to accelerate architecture, implementation, testing, debugging, and documentation. This role requires close collaboration with Design, Quality and Reliability, Product Engineering, Manufacturing, IT, Information Security, and other Intel stakeholders. Responsibilities 1. Architect and develop Intel's reusable AI platform for Design Quality and Reliability. 2. Build AI agents and workflows for engineering data analysis, qualification planning, risk assessment, knowledge retrieval, reporting, and process automation. 3. Apply Agentic AI coding assistants to accelerate software development while maintaining rigorous engineering review and validation. 4. Integrate AI capabilities with Intel engineering databases, quality-management systems, internal APIs, spreadsheets, documentation repositories, and workflow tools. 5. Develop production-grade backend services, APIs, data pipelines, model gateways, and agent-orchestration components. 6. Establish shared platform capabilities for identity, access control, tool authorization, memory, observability, evaluation, and auditability. 7. Implement human approval, deterministic validation, and rollback controls for consequential engineering actions. 8.

Everpure (NYSE: P) has evolved from storage pioneer to data platform, closing fiscal 2026 with $3.7 billion in revenue, its first billion-dollar quarter, and accelerating growth into FY27. Our strategic agenda spans the companies defining the next era of technology - hyperscalers, AI labs, the AI hardware supply chain, data platform providers, and the broader AI ecosystem. This type of work—work that changes the world—is what the tech industry was founded on. So, if you're ready to seize the endless opportunities and leave your mark, come join us. THE ROLE Join the Pure Solutions team as a Senior MLOps Solutions Engineer to architect and build high-scale, enterprise-grade AI/ML solutions. You will be instrumental in integrating Pure Storage platforms with the evolving open-source MLOps ecosystem (Kubeflow, MLflow, Ray) to operationalize the complete machine learning lifecycle. This role requires a creative technologist with deep Python expertise to drive innovation and enable our customers and partners to achieve production AI success. WHAT YOU'LL DO Design and Automate MLOps Pipelines: Lead the development of end-to-end MLOps workflows using CI/CD tools (Git/Jenkins) and orchestration platforms (MLflow/Kubeflow), specifically integrating Pure Storage's FlashBlade, FlashArray, and Portworx as the high-performance data plane for data ingestion, training, and inference. Build High-Performance AI/ML Reference Architectures: Create validated, repeatable deployment models using Infrastructure as Code (e.g., Ansible, Terraform) for AI/ML environments spanning bare metal, virtual machines, and GPU-accelerated Kubernetes clusters, ensuring optimal performance for distributed training. Optimize and Operationalize GPU Inference: Architect and implement solutions for high-throughput, low-latency model serving, utilizing technologies like NVIDIA Triton Inference Server and advanced optimization techniques (quantization, model sharding like DeepSpeed/Megatron-LM, and dynamic bat

- Proven experience deploying and managing Kubernetes clusters for AI/ML workloads. Experience of at scale deployments with Azure Kubernetes. Experience level - 5 Years or more Positions - 2 Proven experience deploying and managing Kubernetes clusters for AI/ML workloads. - Experience of at scale deployments with Azure Kubernetes Service, RedHat OpenShift, Microk8s and Helm Charts. - Expertise with infrastructure and resource management and virtualization tools such as VMWare/EXSi, KVM, Ansible, Redfish. - Strong understanding of Run:AI platform, including job scheduling, quota management, and GPU virtualization. - Knowledge of NVIDIA AI Enterprise components including, NIM, NeMO, TAO, Triton and Nucleus Servers - Familiarity with DGX systems, Jetson, and NVIDIA’s AI Factory components. - Proficiency in Python, C++, and optionally .NET/C# for enterprise integration.

AI Tech Lead – Manager Experience- 8-12 years Job Overview: We are seeking a highly experienced AI Tech Lead to design, develop, and deliver scalable AI-driven applications while leading cross-functional teams. The role involves end-to-end ownership of AI solutions, including architecture design, deployment, and optimization, ensuring alignment with business objectives. The candidate will collaborate with stakeholders, data engineering teams, and product management to build enterprise-grade AI systems leveraging modern cloud and AI technologies. Key Responsibilities / (Person Specifications): Lead implementation and delivery of AI applications across teams. Design end-to-end AI architectures integrating open-source and enterprise tools. Translate business requirements into scalable AI solutions. Define architecture roadmaps and best practices. Build data pipelines, CI/CD, and monitoring systems. Deploy scalable systems using Docker and Kubernetes. Ensure performance, scalability, and security. Mentor teams and drive knowledge sharing. Key Skills / Job Specifications (Mandatory): AI frameworks: LangGraph, AutoGen, CrewAI. Strong Python with TensorFlow, PyTorch, Keras. Knowledge of NLP & Deep Learning (RNN, CNN, LSTM, Transformers). Cloud platforms: AWS / Azure / GCP. Docker, Kubernetes, CI/CD tools. Terraform / CloudFormation (IaC). SQL & NoSQL databases. Distributed systems, REST APIs, GraphQL, microservices.

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. At Okta, we’re building the future of secure, enterprise-grade AI Agents . We’re looking for a Principal Engineer to join our global AI Engineering team. In this role, you will be instrumental in designing and building the intelligent, user-facing experiences and the end-to-end AI solutions that power them. This is a senior individual contributor role for a hands-on engineer who can set technical direction, mentor others, and partner closely with our cross-geo counterparts as part of one global team. What you'll do : Drive the architecture and design of AI solutions, leading cross-functional initiatives across Product, Design, and Data Science. Design, build, and refine the core backend services that power our AI solutions, including LLM orchestration, RAG pipelines, and generative AI features. Build high-performance Agentic Experiences (AX) for web and mobile, engineered for streaming responses and low latency. Champion observability and operational excellence to ensure our AI services meet enterprise-grade standards for reliability and performance. Develop robust backend services to power our AI solutions, including LLM orchestration, RAG pipelines, and generative AI features Enable the successful delivery of key AI projects through technical leadership and hands-on execution. Mentor engineers, raising the bar on technical craftsmanship and solution quality across the full stack. Collaborate with cross-geo peers to ensure globally aligned designs, shared

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. Role Overview & Key Responsibilities This is a high-leverage leadership role that spans architecture, execution, and org-building, and will shape the direction of our AI / ML initiatives at Ema. We are seeking an AI / ML technical leader who can take a vision and build it. As a Principal ML Engineer at Ema, you will be a senior technical leader responsible for shaping the machine learning roadmap, architecting large-scale ML systems, driving innovation, and ensuring our mixture of expert models (LLM + SLM + Custom Model) is accurate and performant at scale. You will collaborate across teams (research, product, infra, data, etc.), mentor senior engineers, and influence strategy and execution at company-wide levels. Responsibilities Lead the technical direction of GenAI and agentic ML systems that power enterprise-grade AI agents — spanning reasoning, retrieval, tool use, and integrations across various SaaS products. Architect, design, and implement scalable production pipelines for model training, fine-tuning, retrieval (RAG), agent orchestration, and evaluation — ensuring robustness, latency efficiency, and continuous learning. Define and own the multi-year ML roadmap for GenA

About Bolna Bolna is a YC-backed voice AI orchestration platform built for the Indian market—powering multilingual, vernacular voice agents across Hindi, Hinglish, Tamil, and 10+ languages at sub-500ms latency across collections, recruitment, sales, and e-commerce use cases. We are an orchestration layer, not a model company: our moat is outcome-labelled vernacular data, rigorous evaluation infrastructure, and a growing taxonomy of how Indian enterprise voice AI fails in production. Why This Role Exists Product decisions at Bolna increasingly hinge on rigorous, code-mixed-aware data analysis—and not just one kind. On one side, there is model and evaluation rigor: LLM benchmarking for post-call intelligence, ASR/WER evaluation, inter-rater reliability on human-labelled calls, and routing and latency economics. On the other, there is product and growth insight: understanding where self-serve users drop off in their journey, what patterns emerge across lakhs of monthly calls, and which use cases and configurations are actually working. Both currently sit with the Head of Product alongside strategy and roadmap ownership. We need a dedicated analyst to own the execution and recurring cadence across both-freeing product leadership to act on findings rather than produce them. What You’ll Do Model and Evaluation Analysis LLM and model benchmarking: Run structured comparisons across model providers such as Sarvam, DeepSeek, Gemini, and Claude variants for tasks including post-call extraction and LLM-as-judge scoring. Evaluate cost, accuracy, fill rate, and TTR, with particular attention to Hinglish and code-mixed content. Evaluation infrastructure: Build and maintain LLM-as-judge pipelines using tools such as DeepEval, design and track evaluation metrics, and run inter-rater reliability analysis such as Krippendorff’s alpha across human call reviewers. Golden dataset creation: Support the construction of golden datasets for ASR and transcript labelling, including flagging co

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Data Scientist II Role Overview Build and productionize enterprise-grade AI and Generative AI solutions for Mastercard. This role combines strong software engineering with model fine-tuning, Databricks-based ML engineering, AWS deployment, and end-to-end MLOps. Key Responsibilities Design, develop, test, and maintain scalable AI/ML applications, APIs, and reusable engineering components. Fine-tune and evaluate foundation models using techniques such as LoRA, QLoRA, PEFT, and supervised fine-tuning. Build RAG solutions, embeddings workflows, vector-search applications, and AI agents. Create end-to-end ML pipelines for data preparation, training, evaluation, deployment, monitoring, and retraining. Use Databricks, PySpark, MLflow, Unity Catalog, Workflows, Vector Search, and Model Serving for governed model development and operations. Implement CI/CD, automated testing, observability, model monitoring, and production support practices. Partner with data science, engineering, product, security, privacy, and governance teams to deliver reliable and responsible AI solutions. Required Skills & Experience 3–4 years of experience in AI/ML engineering, software engineering, data science, or a related field. Strong Python, SQL, object-or