Distributed Systems Engineer Data Platform Delivery Database Retrieval in India

Here at Appian, our values of Intensity and Excellence define who we are. We set high standards and live up to them, ensuring that everything we do is done with care and quality. We approach every challenge with ambition and commitment, holding ourselves and each other accountable to achieve the best results. When you join Appian, you’ll be part of a passionate team dedicated to accomplishing hard things, together. Drives AI-first tooling, SDLC automation, and developer productivity initiatives Required Experience & Skills 6+ years of experience building internal tools or developer platforms Strong CI/CD and automation expertise, Generative AI integration in SDLC Core Backend & Architectural Fundamentals: Deep dive into data structures, algorithms, and microservices/distributed system architecture patterns. While we embrace a language-agnostic mindset, strong proficiency in Java or Python is preferred. Cloud & Infrastructure: Hands-on experience with cloud infrastructure, container frameworks (Docker/Kubernetes), and CI/CD pipelines is a major advantage Modern Engineering Practices (Highly Valued): Daily utilization of AI tools and agentic engineering environments (e.g., Kiro, Claude, Copilot) to accelerate software design, automate background workflows, and optimize developer productivity Tools and Resources Training and Development: During onboarding, we focus on equipping new hires with the skills and knowledge for success through department-specific training. Continuous learning is a central focus at Appian, with dedicated mentorship and the First-Friend program being widely utilized resources for new hires. Growth Opportunities: Appian provides a diverse array of growth and development opportunities, including our leadership program tailored for new and aspiring managers, a comprehensive library of specialized department training through Appian University, skills based training, and tuition

Roles and Responsibilities Write maintainable/scalable/e?cient code. Work in a cross-functional team, collaborating with peers during the entire SDLC. Follow coding standards, unit-testing, code reviews etc. Follow release cycles and commitment to deadlines. Qualifications & Experience Experience level of 3 to 5 years of experience in very large scale applications. Fair understanding in problem solving skills, data structures and algorithms. Experience with distributed systems handling large amounts of data. Fair understanding in coding skills in Java/J2EE, Web technologies, RDBMS/messaging.

Roles and Responsibilities Write maintainable/scalable/code Work in a cross-functional team, collaborating with peers during the entire SDLC. Follow coding standards, unit-testing, code reviews etc. Follow release cycles and commitment to deadlines. Qualifications & Experience Experience level of 3 to 6 years of experience in very large scale applications. Fair understanding in problem solving skills, data structures and algorithms. Experience with distributed systems handling large amounts of data. Fair understanding in coding skills in Java/J2EE, Web technologies, RDBMS/messaging.

Roles and Responsibilities Write maintainable/scalable/e?cient code. Work in a cross-functional team, collaborating with peers during the entire SDLC. Follow coding standards, unit-testing, code reviews etc. Follow release cycles and commitment to deadlines. Qualifications & Experience Experience level of 3 to 5 years of experience in very large scale applications. Fair understanding in problem solving skills, data structures and algorithms. Experience with distributed systems handling large amounts of data. Fair understanding in coding skills in Java/J2EE, Web technologies, RDBMS/messaging.

Forward is transforming how the world’s most complex networks are managed and secured. Founded in 2013 by four Stanford Ph.D.s, we built the industry’s first network digital twin — a mathematically precise model of the production network that gives IT teams unmatched visibility, verification, and agility across every major cloud and vendor environment. Our customers include global leaders such as Goldman Sachs, PayPal, S&P Global, IBM, and Dell, as well as fast-growing enterprises and government agencies. According to IDC, Forward customers realize an average of $14.2 million in annual benefits through improved efficiency and security. Backed by world-class investors including Andreessen Horowitz, Goldman Sachs, MSD Partners, and Threshold Ventures, Forward offers a people-centric, innovative culture where brilliant minds are shaping the future of network reliability, security, and AI-ready operations. Forward is currently seeking experienced Java developers to work as part of our Network team. Responsibilities Help bring the best ideas from the software development world into the networking industry. Contribute to our code base, systems and software architecture as a member of our engineering team. Help create and optimize network device models for different device vendors and protocols. Help create infrastructure needed to configure, collect and test network devices. Work with peers who are experts in Networking, Distributed Systems, Big Data and Search. Requirements 5+ years of work experience in software development 3+ years of work experience with Java BS in Computer Science or related degree Solid software engineering experience with large code bases Basic understanding of networking and TCP/IP. Strong verbal and written communication skills. Nice to haves Working knowledge of how switches, routers, firewalls or load balancers work. Experience working with networking protocols such as BGP/OSPF/IS-IS, IPv4/IPv6, MPLS, VLAN, VXLAN, etc. This position is a re

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

JD : Full Stack with React ,HTML ,CSS 4+ working experience in ReactJs, Html 5, CSS, Responsive design on UI , and Springboot, REST APIs in backend. Experience with Java web application development, Microservices architecture and distributed cloud systems. Experience with REST APIs using Spring boot, Spring data, Spring cloud config, Spring AMQP connector or similar framework in microservices development. Hands on with SQL commands stored procedures. Hands on experience with CI/CD pipelines and setting up various code quality checks in the pipelines. Skills Required UI React JS HTML 5 ,CSS 3 JEST framework Material UI Responsive Design Backend Java/J2ee, Spring MVC, Spring Boot, Micro services Architecture & design Patterns, Docker containers SQL Basic Queries , Stored Procedures AWS EC2/ECS,Lambdas,S3, RDS, SQS

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and

About the Role Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide. In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring. The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones. Key Responsibilities Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences. Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent). Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs. Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization. Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and

Role: Application Reliability Engineer Location: Gurgaon Who we are Graviton Research Capital is a privately funded quantitative trading firm striving for excellence in financial markets research. We trade across a multitude of asset classes and trading venues using a diverse range of concepts, from time series analysis and stochastic models to machine learning and statistical inference. We analyse terabytes of data to identify pricing anomalies and drive innovation in financial markets. Key Responsibilities and Deliverables The ideal candidate will possess a strong background in technical support, with a passion for problem-solving and a commitment to excellence. As an Application Reliability Engineer, you will be responsible for: Monitor production services and respond quickly to alerts, incidents, and outages to ensure smooth operation and minimal downtime. Monitor trading systems and infrastructure., Triage issues across trading support services, databases, and infra; escalate and coordinate with the right owners, and drive root-cause analysis and ensure fixes are implemented for long-term stability. Serve as the first line of defense for trading operations. Proactively identify, address recurring issues, and build automation to reduce manual intervention. Improve observability by enhancing monitoring, logging, and alerting systems. Develop and maintain operational runbooks and SLO/SLA metrics. Eligibility and Required Skills Possess a degree in a highly analytical field, such as Engineering, or Computer Science 2-5 years of experience in Python, Shell/Bash scripting. Experience with Linux and shell/bash online tools. Hands-on experience with databases (SQL, NoSQL) Strong problem-solving and analytical skills Excellent communication skills Ability to remain calm and analytical under production pressure Good to have: Familiarity with monitoring/alerting stacks (Prometheus, Grafana, ELK, etc.) Familiarity with distributed messaging (Kafka) and caching systems (Red

At Bolna, we’re building tools that change the way teams leverage Voice AI. We’re looking for a Founding Machine Learning Engineer to own the end-to-end lifecycle of building, evaluating, deploying, and improving models that power millions of production conversations. This is a high-impact, high ownership role where you won’t just work on Bolna’s ML stack—you’ll help build the foundation it scales on. Our team includes IIT alumni with experience at Bain, Atlassian, Uber, Zomato, and LinkedIn, and is backed by leading investors. Responsibilities: Build the data engine - Design pipelines to source and clean conversational voice data across Indian languages, accents, and telephony conditions. Fine-tune models that ship - Fine tune and train models to improve accuracy, speed, and reliability across different use-cases. Define what "good" means - Build evaluation datasets and benchmarks for transcription accuracy, voice naturalness, interruption handling, latency, and end-to-end conversation quality. Set up human-in-the-loop pipelines to capture subjective quality at scale. Ship to production - Work with the engineering team to deploy models into a latency-sensitive, high-volume system. Monitor performance in the wild, debug regressions, and iterate fast. Required Skills: 3+ years of hands-on ML experience with deep practical real-world experience in training models. Strong Python and PyTorch fundamentals with exposure in distributed training, and modern fine-tuning techniques (LoRA, QLoRA, DPO, RLHF, etc.). Training data as a first-class problem. Experience designing data pipelines from collection, cleaning, labeling, deduplication, augmentation and treating data quality as a core engineering discipline. Rigorous about evaluation. You know that "looks good in a demo" is not a benchmark. You build the evals before you trust the model. Speech model experience is a plus with real-time / streaming inference experience where you would have contributed to latency optimization

About the Role Together AI runs one of the largest GPU fleets in the world. The Infra Agent Systems team builds the software systems that power and automate that infrastructure. We develop production AI agents that diagnose hardware failures, investigate incidents, correlate signals across the fleet, and automate operational workflows. Alongside these agents, we build the platform they run on, including knowledge graphs, retrieval systems, orchestration frameworks, and developer tooling. You’ll work across two areas: Infrastructure Agent Systems — Build production AI agents that help operate our GPU fleet by diagnosing failures, investigating incidents, gathering evidence from live systems, and assisting with remediation. These agents are used every day by our infrastructure and datacenter teams through APIs, CLI, dashboards, and Slack. Core Agent Platform — Build the platform that powers these agents, including knowledge graphs, search and retrieval, orchestration, evaluation, and the tooling that enables agents to reason, act, and continuously improve. We’re working on something that hasn’t really been done before: building knowledge graphs and self-improving AI agents that understand, operate, and continuously improve large-scale AI infrastructure. This is an opportunity to work at the intersection of AI agents, distributed systems, infrastructure, and automation , solving challenging engineering problems with real production impact. There’s an enormous amount to build, learn, and shape as we define the future of autonomous infrastructure. responsible for delivering the software but also for operating and supporting it in production. Why this Role You’ll work on two hard problems at the same time: making AI agents trustworthy enough to operate production infrastructure, and building the knowledge, retrieval, and distributed systems that make those agents effective. You’ll have the opportunity to build foundational systems from the ground up, work on infrastructur

SonicWall is a cybersecurity forerunner with more than 30 years of expertise and is recognized as a leading partner-first company, ensuring our partners and their customers are never alone in the fight against cybercrime. With the ability to build, scale and manage security across the cloud, hybrid and traditional environments in real-time, SonicWall provides relentless security against the most evasive cyberattacks across endless exposure points for increasingly remote, mobile and cloud-enabled users. With its own threat research center, SonicWall can quickly and economically provide purpose-built security solutions to enable any organization—enterprise, government agencies and SMBs—around the world. For more information, visit www.sonicwall.com or follow us on Twitter , LinkedIn , Facebook and Instagram . As a Software Dev Senior Engineer , you will own the reliability, scalability, and operational excellence of our Cloud-based services. You will define and enforce reliability standards, drive the adoption of SRE practices across engineering teams, and build the systems and tooling that keep our production infrastructure healthy. We follow a DevOps model: Development and Operations teams are integrated, and the SRE function acts as the reliability layer — setting Service Level Objectives, managing error budgets, and continuously reducing toil through engineering. Key Responsibilities: Define, publish, and continuously refine Service Level Indicators (SLIs), Service Level Objectives (SLOs), and Service Level Agreements (SLAs ) for all critical services, partnering with product and engineering leadership. Own the error budget framework: track consumption, enforce error budget policies, and drive reliability investments when budgets are at risk. Lead the design and implementation of comprehensive observability platforms — metrics, structured logging, and distributed tracing — to ensure full visibility into pro

About the Role At Jumio, the Software Development Engineer IV - QA (SDE-IV, QA) is a senior technical role focused on ensuring the quality, performance, and reliability of highly scalable web portals and distributed backend systems. Our platform spans multiple Java Spring Boot microservices and customer-facing web portals , deployed across AWS ECS, EKS, and Lambda , and integrated through event-driven messaging using SNS/SQS . In this role you will design and drive the test automation strategy for both UI (Playwright) and API/service layers, set the quality bar for the team, and act as a force multiplier — mentoring other engineers and embedding quality earlier in the development lifecycle. You will work closely with development, product, and DevOps teams to ensure our products meet the highest standards of quality, scalability, and security. This is a hands-on senior IC role: you will write code, but you will also influence architecture, own cross-service test strategy, and make build-vs-buy decisions for testing tooling. T-Shaped Engineering Expectation As part of Jumio's engineering culture, you will adopt a T-shaped engineering approach. Beyond deep expertise in test automation and quality engineering, you will contribute across the development lifecycle — understanding software architecture, participating in design and API-contract discussions, reviewing application code, and ensuring our distributed systems are testable, observable, and resilient by design. Role Value This role is critical to ensuring the reliability, scalability, and security of Jumio's products. By architecting and maintaining automated testing frameworks across web, API, and event-driven layers, you will enable faster, higher-confidence releases and reduce production risk in a complex microservices environment. What You'll Do Test Architecture & Strategy Define and own the end-to-end automated test strategy across web portals and backend microservices, balancing UI, API, contract, integ