Top Machine Learning Jobs in Chennai

Develop scalable data science and machine learning solutions using Python, SQL, statistics, cloud technologies, and business data. Responsibilities include extracting and analyzing data, identifying business opportunities, building dashboards and models, operationalizing AI/ML solutions, integrating models into business processes, and communicating insights to stakeholders. The role supports applications across connected vehicles, smart mobility, manufacturing, supply chain, logistics, and warranty analytics.

Serve as the first point of contact for customers, partners, and consultants. Research, troubleshoot, replicate, and test product issues; track incidents and provide resolution updates. Collaborate with Development, QA, and Consulting Services, occasionally support AI-related initiatives, and participate in a weekend rotation for 24/7 emergencies.

Design and build AI-powered quality engineering systems using autonomous agents, LLMs, and modern test automation. Develop self-healing frameworks, automate web and mobile customer journeys, identify release risks, analyze defects, and validate streaming experiences across devices. Collaborate cross-functionally on software development, releases, troubleshooting, and quality assurance. The role requires hands-on expertise with multiple automation frameworks, programming languages, CI/CD tools, AI technologies, and streaming or OTT platforms.

Sell SailPoint's IGA identity security SaaS to $5B+ enterprises by engaging C-level stakeholders, managing long sales cycles with POCs/RFPs, collaborating with GSIs and channel partners, developing account and opportunity plans, and meeting quota using Challenger methodology.

Leads engineering delivery for enterprise conversational AI platforms, including chatbots and voice bots. Oversees Agile execution, technical architecture, cloud-native development, CI/CD, Terraform, DevSecOps, Responsible AI, and AI adoption. Partners with product, security, architecture, and business stakeholders while managing risks and dependencies. Mentors engineers and technical leads, supports hiring and performance management, and ensures scalable, secure, compliant, and operationally excellent solutions.

Design and maintain scalable ETL/ELT pipelines, data warehouses, and big data infrastructure. Optimize databases and workflows, implement data quality and governance frameworks, automate testing and deployment, troubleshoot complex data issues, and ensure reliable systems. Collaborate with data scientists, analysts, and business teams to support analytics and machine learning. Mentor junior engineers and establish data engineering best practices.

Assists with designing, developing, testing, deploying, and maintaining cloud-based software and AI services. Builds microservices and REST APIs, works with structured and unstructured data, supports ML/AI algorithms, automates integrations, and contributes to distributed systems, CI/CD, documentation, code reviews, and debugging. Collaborates with product teams and stakeholders in an agile environment while supporting software delivery across multiple cloud platforms.

Develop and test software components for cloud-based and internet-related products in a microservices environment. Build APIs, process telemetry and structured or unstructured data, apply ML/AI algorithms, and support distributed systems, automation, deployments, testing, documentation, and code reviews. Collaborate with product teams and stakeholders throughout the software delivery lifecycle using Agile, DevOps, CI/CD, and cloud infrastructure practices.

Develops, tests, deploys, and maintains software in cloud-based, microservices environments. Builds REST APIs, integrates third-party systems, works with structured and unstructured telemetry data, and applies ML/AI algorithms. Supports distributed systems, automation, CI/CD, code reviews, debugging, refactoring, documentation, and software delivery across Agile teams and multiple stakeholders. The role also contributes to prototypes, deployments, automated testing, and collaboration with product teams.

Develop and test AI-enabled software services in a microservices environment. Build APIs, process structured and unstructured telemetry data, apply ML/AI algorithms, and support distributed systems, cloud deployments, automation, CI/CD, testing, documentation, code reviews, and refactoring. Collaborate with product teams and stakeholders across software delivery phases while working with AWS, GCP, Azure, containers, and SQL/NoSQL data stores.

Designs, develops, tests, troubleshoots, and maintains scalable software services for customer accounts, identities, orders, and delivery workflows. Applies machine learning solutions, distributed systems principles, security practices, and cloud technologies. Collaborates on requirements and engineering best practices while improving system performance, scalability, fault tolerance, automation, and deployment processes.

Leads the design, development, testing, deployment, and maintenance of AI-enabled software in cloud-based, microservices environments. Develops RESTful APIs, distributed systems, and ML/AI solutions using structured and unstructured data. Collaborates across teams, performs code reviews, automates testing and deployments, refactors code, and documents technical and operational procedures. Requires expertise in cloud platforms, programming, databases, containers, CI/CD, Agile practices, and full software delivery lifecycles.

Leads the design, development, testing, deployment, and refactoring of cloud-based software in microservices environments. Develops APIs, distributed systems, automation, and ML/AI solutions using structured and unstructured data. Collaborates across geo-distributed teams, reviews code, supports CI/CD and automated testing, and contributes to technical documentation and operational procedures.

Leads the design and implementation of AI-powered knowledge engineering solutions, including ontologies, knowledge graphs, semantic models, graph-based machine learning, and enterprise LLM applications. Establishes technical standards, designs data architectures and pipelines, guides cross-functional teams, mentors engineers, supports client relationships and pre-sales, and provides thought leadership on AI, knowledge representation, and emerging technologies.

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.

Leads the architecture, development, and governance of enterprise-scale cloud data platforms on GCP. Designs data lakes, warehouses, marts, ETL/ELT frameworks, batch and event-driven pipelines, and analytics solutions. Provides technical leadership, mentoring, code and architecture reviews, and engineering standards. Ensures data quality, security, observability, monitoring, and governance while optimizing BigQuery performance and cloud costs. Collaborates with stakeholders to translate business requirements into scalable data products and supports production troubleshooting.

Owns technical testing of AI agent outputs by building test suites against golden-record datasets, measuring accuracy and turnaround-time impact, and running regression tests after agent or prompt changes. Reports testing metrics for business baselines and provides technical evidence to human QA teams for final sign-off in regulated processes.

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.

Develop and optimize image processing, computer vision, machine learning, and deep learning algorithms for semiconductor inspection. Prototype concepts in Python and implement production-quality solutions in C++. Lead features from requirements through customer deployment, troubleshoot field issues, perform root-cause analysis, and document technical findings.

Lead design, develop, test, and deploy scalable Java/Spring Boot cloud-native applications on AWS/Azure. Integrate AI/ML, build full-stack solutions with ReactJS, enforce code quality, mentor engineers, champion DevOps/security practices (Kubernetes, Docker, CI/CD, Git), and design compliant KYC/AML banking solutions.

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.

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.

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.