Top Machine Learning Jobs in Chennai

Implement machine learning models in production and design enterprise GenAI solutions, AI agents, and scalable LLM/ML automation. Build and manage industrialized processing pipelines, MLOps and LLMOps frameworks, and model lifecycle best practices. Integrate Microsoft Copilot Studio and Azure AI services with Power Platform tools, implement RAG and knowledge grounding, support multilingual retrieval, and troubleshoot agents. Gather requirements, estimate work, present technical solutions, and create documentation.

Build, train, and optimize machine learning and GenAI models, including RAG and LLM integrations. Develop production-grade Python and FastAPI services, scalable APIs, and cloud-based ML solutions across GCP, Azure, and AWS. Support cloud migration, ML platform modernization, CI/CD implementation, API integrations, and modular object-oriented design while collaborating with product, data engineering, and infrastructure teams.

Build scalable, low-latency backend APIs, event-driven services, integration layers, and microservices supporting personalization and recommendation experiences. Partner with AI/ML and data engineers to deploy models, expose AI services, and develop React applications. Improve security, observability, reliability, performance, scalability, and operational efficiency while using AI-assisted engineering tools to enhance delivery and code quality.

Leads a digital consultancy practice by partnering with sales, developing client relationships, overseeing strategic technology programs, and expanding innovative offerings. The Director provides thought leadership, manages engagement profitability and delivery quality, develops business cases and technical roadmaps, and mentors consultants, project managers, and architects. Responsibilities also include recruiting, performance management, client presentations, proposal development, and guiding complex enterprise solution implementations.

Lead the architecture, development, deployment, and operation of AI/ML solutions for identity and access security. Build scalable pipelines, inference services, monitoring, and MLOps capabilities using cloud platforms. Integrate anomaly detection and behavioral analytics into IAM, PAM, and secrets-management workflows. Partner with cybersecurity and engineering teams, establish applied AI security standards, influence technical roadmaps, and mentor junior engineers.

Analyzes US healthcare claims and related datasets to identify data-quality issues, patterns, coverage gaps, and product opportunities. Develops SQL queries, dashboards, validation processes, documentation, and test scenarios; investigates defects and performs root-cause and impact analysis. Collaborates with product, engineering, machine learning, engagement, and account teams to support healthcare data-product delivery, client data services, product enhancements, and proof-of-concept initiatives.

Leads emerging product innovation from opportunity discovery through prototyping, pilots, product-market validation, commercialization, and scale. Owns AI, data, analytics, automation, and platform product strategies; establishes experimentation and stage-gate governance; develops investment cases and go-to-market plans; and aligns cross-functional teams and executives. Manages product leaders, builds incubation practices, and drives measurable customer, revenue, efficiency, and strategic outcomes.

Design, build, deploy, and operate production AI/ML systems on Google Cloud. Develop clean, tested Python; evaluate model quality; manage cloud networking and security; and ensure data and models remain within client boundaries. Explain AI trade-offs to clients, collaborate with Google Cloud teams, and own outcomes from prototype through reliable production use.

Build reinforcement learning environments for coding tasks, develop and deploy production AI agents, and shape technical direction. The role also involves leading engineering growth in a fast-paced startup. Candidates need 3–7 years of engineering experience, strong reinforcement learning fundamentals, experience with coding-focused reinforcement learning, simulated training environments, autonomous agents in production, and AI coding assistants. The position is remote in India with a 5.5-day workweek and Pacific-time overlap.

Lead end-to-end ML systems: data pipelines, model development (transformer-based detection), sim-to-real synthetic data, multi-sensor fusion, on-device real-time inference (Jetson), MLOps (experiment tracking, versioning), and production deployment and monitoring. Set ML roadmap and hire/lead as the team grows.

Develop and deploy AI, generative AI, and LLM solutions that support credit risk modelers. Build and validate credit risk models using GCP, SAS, R, and Python; apply statistical and machine learning techniques; analyze results; document modeling processes; support model development; conduct ad hoc analytics; and implement secure, governed AI tools for analytical workflows.

Lead research on improving AI agent training data and evaluations. Manage and mentor research engineers, define research direction, design rigorous experiments, validate data quality, diagnose reward and grading failures, and translate findings into scalable tools, workflows, and standards. Collaborate with engineers, domain experts, and vendors while communicating technical conclusions and tradeoffs clearly.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

Develop and refine voice-optimized prompts and agentic LLM workflows for finance use cases. Build RAG grounding, integrate LLMs with voice/telephony (LiveKit), implement feedback loops, A/B testing, fine-tune models, and collaborate on production ML services and OCR/chatbot components.

Lead technical direction and delivery for a core NIRA backend domain: design and ship high-volume web APIs, own architecture for distributed and queue-based systems, drive complex initiatives, mentor engineers, lead incident response, champion AI dev tools, and align cross-functionally to simplify systems and influence roadmap.

Develop quantitative models for cryptocurrency derivatives and spot markets, conduct statistical and econometric analysis, build pricing and risk models, and backtest systematic trading strategies. Collaborate with trading and product teams to validate and deploy models, monitor performance, analyze high-frequency data, and communicate research insights to leadership and trading desks.

Build and operate production AI/ML systems, including LLM applications, RAG pipelines, automated model training, APIs, multimodal data pipelines, and MLOps workflows. Deploy and monitor solutions on AWS using SageMaker, Lambda, S3, Glue, EKS, and Bedrock. Implement responsible AI guardrails, data governance, security, CI/CD, model versioning, performance monitoring, and retraining processes.

Train and deploy small, task-specific ML models for realtime English conversational-skill applications. Create datasets and task-specific evaluations, improve internal SOTA, develop ASR and speech-feedback metrics, iterate experiments, ship production systems, and collaborate cross-functionally while helping hire as the team grows.

Develop artist-facing and backend tools that extend SideFX Houdini and integrate with VFX and CG production pipelines. Build clean, testable, maintainable software using HOM, HDK, Python, and/or C++. Collaborate with artists, developers, and project managers across the software lifecycle, while emphasizing usability, documentation, UX, reliability, and adoption. Support procedural workflows, production tools, simulations, rendering, and distributed studio systems.

Build Python-based tools for layout, animation, matchmove, and previs artists working with complex CG scenes. Develop scene-management systems, asset and animation library integrations, and 3DEqualizer data import/export workflows. Collaborate with product owners and stakeholders in an Agile environment, contributing across design, development, maintenance, and pipeline integration. Work may include front-end and back-end development for high-end VFX and feature animation pipelines.

Develop artist-facing and backend tools that extend Autodesk Maya and integrate with VFX and CG pipelines. Build reliable, maintainable software using HOM, HDK, Python, C++, MEL, and Maya APIs; improve workflows, usability, and adoption; and collaborate with artists, developers, and project managers throughout the software lifecycle. The role also involves documentation, testing, deployment, support, roadmap planning, and innovation across distributed VFX production teams.

Leads strategy, roadmap, and end-to-end development of ML-powered generative AI products. Defines requirements, prioritizes backlogs, integrates ML models into scalable experiences, tracks product metrics, evaluates emerging technologies, and coordinates cross-functional teams spanning ML research, engineering, data, UX, and creative functions.

Conduct independent research on frontier AI evaluation and auditing. Design rigorous evaluations, audit methods, and reproducible experimental pipelines; analyze model capabilities, risks, robustness, and alignment-relevant behavior. Produce audit reports, recommendations, publications, tools, and presentations while collaborating with technical, policy, engineering, and standards teams. The role requires careful handling of confidential information and may involve pilot audits with frontier AI companies.

Lead engineering strategy, teams, architecture, delivery, and platform outcomes for the Unify Energy SaaS platform. Oversee software, data, integrations, automation, security, reliability, and applied AI capabilities supporting oil and gas information workflows. Partner with product, customers, implementation, and business leaders to prioritize investments, improve delivery predictability, strengthen platform quality, and achieve measurable customer, operational, and financial results.

Build privacy and anonymization systems that detect and transform PII, credentials, secrets, and other sensitive information in data used for AI training. Develop detection methods combining rules, statistical models, classifiers, and LLMs; create production data-processing pipelines; evaluate privacy risk and retained utility; and ensure robustness against schema drift, unusual formats, leakage, and adversarial re-identification.