Top Machine Learning Jobs in Chennai

Develop AI-powered product features for local discovery, including personalized recommendations, content curation, search, and ranking. Build data pipelines by scraping, cleaning, and structuring datasets for machine learning models. Collaborate with product, design, and engineering teams to integrate AI into the platform, while continuously improving machine learning capabilities and user experiences.

Seeking senior software engineers with proven expertise in at least one technical area, including AI/ML, LLMs, data, full-stack or backend development, automation, cloud infrastructure, and DevOps. Responsibilities include solving complex problems independently, collaborating across technologies, and delivering production-quality projects in a startup environment. Engagements may be project-based or ongoing.

Designs and develops enterprise software and cloud services in C++, focusing on Windows/Linux internals, high-performance scalable systems, virtual desktops, and AI/ML integration. Leads architectural initiatives, participates across the software development lifecycle, improves DevOps and CI/CD processes, troubleshoots complex production issues, supports customer escalations, and mentors junior engineers. The role is hybrid in Chennai or Bangalore with approximately 10% domestic or international travel.

Design, build, and operate production machine learning systems for Bloomreach’s search platform. Responsibilities include query-time ML, LLM and catalog data pipelines, model serving, online features, evaluation, A/B testing, observability, and low-latency optimization across merchants, languages, and domains. The role partners with Search Quality engineers to deploy ranking and relevance models reliably and cost-effectively at scale.

Build, evaluate, and deploy production machine learning models for utility data extraction, forecasting, anomaly detection, and agent-powered workflows. Responsibilities include Python development, experimentation, model evaluation, data validation, prompt tuning, documentation, monitoring support, and collaboration with engineering and subject matter experts.

Lead the development, training, deployment, and maintenance of machine learning and generative AI systems. Build predictive, NLP, chatbot, LLM, and agentic AI solutions using cloud platforms, big data technologies, databases, and ML frameworks. Design ETL pipelines, optimize data processing, and create analytics visualizations. The role also involves model fine-tuning, scalable AI application development, and collaborative problem-solving.

Leads a technical team developing computer vision, machine learning, image processing, and deep learning algorithms for semiconductor inspection and metrology systems. Defines architecture and technical roadmaps, drives high-performance CPU/GPU implementations, oversees validation and product releases, resolves customer issues, and partners with global software, HPC, systems, product, and applications teams. Responsible for hiring, mentoring, succession planning, and cross-functional delivery.

Supports semiconductor inspection products through customer demonstrations, product characterization, troubleshooting, training, documentation, and technical collaboration. Investigates manufacturing yield issues, develops test plans, evaluates tool and algorithm performance, communicates product value, and supports beta testing, tool ramp, and customer adoption. Requires frequent global customer interaction and more than 50% travel.

Lead the architecture and development of scalable software for semiconductor manufacturing automation. Build across frontend, backend, databases, messaging, infrastructure, Kubernetes, and AI-powered systems. Partner with product teams, customers, and domain experts to improve manufacturing productivity, quality, throughput, and reliability. Mentor engineers, guide technical direction, improve observability and performance, and influence product decisions through strong engineering judgment and semiconductor manufacturing expertise.

Leads clinical systems teams and oversees RPA, system integration, and AI/ML projects. Ensures study goals, timelines, quality standards, client requirements, and contractual obligations are met. Provides technical support, escalation management, contingency planning, resource utilization oversight, and people leadership, including coaching, performance management, interviewing, and mentoring. Supports customer relationships and delivery teams while managing junior professionals.

Own the strategy, roadmap, delivery, and growth of an agentic AI platform for enterprise automation. Lead customer discovery, market validation, prioritization, agile execution, cross-functional collaboration, product demos, and AI governance. Translate enterprise pain points into scalable features while partnering with engineering, AI/ML, design, sales, marketing, and solutions teams.

The Customer Success Business Partner at Lyric drives customer adoption and retention for strategic accounts, leveraging expertise in AI and supply chain operations.

Contribute to a live Yard Management System through computer vision and OCR prototyping or backend feature development. Responsibilities include building and evaluating detection pipelines, benchmarking models, assessing edge deployment, implementing workflow and inventory features, writing tested code, participating in reviews, documenting work, and developing automation and integrations.

Build and productionize machine learning, LLM, and AI solutions with Data Science teams. Design industrialized data-processing pipelines, implement MLOps and LLMOps frameworks, manage model lifecycles, and develop production Python, ML, and cloud solutions using Azure, GCP, Databricks, Spark, and PySpark. Gather requirements, estimate work, document solutions, present results, and support technical best practices.

Serve as a technical expert throughout enterprise sales cycles, partnering with account executives and customers to identify requirements, demonstrate SingleStore solutions, develop and deploy proofs of concept, lead technical evaluations, tune queries, conduct load testing, and support account expansion and channel partnerships. The role requires expertise in distributed databases, data platforms, AI and ML, cloud and containerized environments, programming, and enterprise technology sales.

Work on ML/DL and computer vision models for aerospace and defense (missiles, rocket telemetry, avionics). Train, test, and improve models; prepare and clean text/image/sensor datasets; build lightweight demo APIs with FastAPI/Flask; document experiments and collaborate with mentors on real-world avionics systems.

Designs, develops, and maintains scalable software solutions integrating AI and machine learning models into commercial products and internal processes. Collaborates with data scientists, product managers, and cross-functional stakeholders; writes high-quality code; reviews architectures; supports product launches and process improvements; and follows Agile, NPI, and deployment best practices.

Lead the design, implementation, deployment, and operation of machine-learning platform capabilities. Build scalable systems for model serving, feature computation, data validation, workflow orchestration, monitoring, and production support. Own complex initiatives and investigations end to end, improve reliability and developer experience, mentor engineers, conduct design reviews, and guide technical strategy while partnering with Data Science, Product, Risk, Fraud, and Engineering teams.

Lead global MLOps delivery by designing scalable software architectures, data pipelines, and machine learning solutions. Guide architecture decisions, collaborate with research, architecture, and product teams, and coordinate software delivery. Manage and mentor developers, conduct code reviews, maintain documentation, promote engineering best practices, oversee testing and CI/CD, and remain accountable for technical debt and system quality.

Develop, validate, debug, and optimize machine learning models, training pipelines, inference systems, and numerical implementations using Python. Build reproducible workflows, diagnose computational issues, create systematic tests, and improve memory usage, throughput, and performance. Review code and solutions, use coding agents in development, document technical findings, and deliver reliable work independently within project deadlines.

Design, build, and scale production AI agent workflows using AWS Bedrock AgentCore, Step Functions, Claude, MCP, RAG, and guardrails. Develop Python/FastAPI backends, agent-facing interfaces, document-processing pipelines, enterprise integrations, and AWS infrastructure. Implement observability, evaluations, security controls, human approval workflows, and CI/CD. Work directly with enterprise customers and nontechnical stakeholders to deliver end-to-end AI solutions with minimal guidance.

Design and deliver production-grade AI/ML architectures for education and assessment platforms. Translate stakeholder requirements into secure, cost-aware blueprints covering RAG, multi-agent systems, LLM judging, OCR, human review, cloud infrastructure, LLMOps/MLOps, observability, governance, and evaluation. Lead workshops, architecture decisions, production readiness, FinOps, sovereign hosting, and technical mentorship across international government and education programs.

Design, develop, and deploy ML models on Google Cloud (Vertex AI). Process and feature-engineer large datasets in BigQuery, integrate ML pipelines with GCP services, collaborate with stakeholders, and produce analytical reports and visualizations to support data-driven decisions.

Own Tala’s product strategy and roadmap for fraud prevention, authentication, identity, and risk capabilities. Partner with Engineering, Data Science, Analytics, and business teams to build data-driven fraud products, define decisioning metrics and thresholds, manage fraud incidents, improve customer security, and minimize friction. Lead cross-functional execution from discovery through launch and iteration while managing external technology partners.

Implement machine learning models in production and design enterprise GenAI, LLM, and AI automation solutions. Build and manage industrialized processing pipelines, establish ML lifecycle and MLOps/LLMOps best practices, integrate Microsoft AI and Power Platform services, and support data science teams. The role also involves gathering requirements, estimating work, presenting technical solutions, troubleshooting AI agents, and creating documentation.