Top Machine Learning Jobs in Chennai

Leads data engineering teams and provides technical direction for scalable, reliable data platforms and solutions. Oversees Agile delivery, backlog planning, operational support, quality standards, technical debt reduction, and stakeholder communication. Coaches engineers, sets performance goals, drives innovation, and guides adoption of data, cloud, containerization, DevOps, artificial intelligence, and machine learning technologies.

Designs, implements, validates, and deploys machine learning models and data pipelines. Develops data ingest/validation/cleaning/monitoring, evaluates vendor solutions, authors technical documentation, and mentors junior engineers.

Lead scientific and strategic AI efforts for Vaccines Medical: define AI vision and roadmap, govern responsible AI standards, prioritize and deliver validated AI-enabled solutions, partner cross-functionally, oversee deployment and adoption, manage budget and vendors, and ensure regulatory, ethical, and scientific compliance across global medical affairs.

Build data-driven solutions and BI using Python, Power BI, and SQL. Implement DevOps pipelines with GitHub Actions/Azure DevOps, deploy on Azure/AWS, integrate APIs, apply unit testing and automation, and collaborate in Agile/Scrum with global teams. Support solution design, effort estimates, and use ML/DL/LLMs for intelligent features.

Serve as primary customer contact for FourKites' SaaS platform: troubleshoot, replicate, and track product issues; coordinate with Development, QA, and Consulting; provide regular client updates; participate in weekend emergency rotation; and contribute to AI-driven support initiatives.

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.

Develop AI products on Azure and Optum AI platforms using Python, Spring, Java, and React. Lead a team of engineers delivering AI solutions, support production deployments, and ensure compliance with company policies. The role requires a graduate degree or equivalent experience, at least five years working with the specified technologies, strong knowledge of AI, Azure, React, Java, Oracle, and MySQL, with relevant certifications preferred.

Designs and maintains automated tests for front-end, middleware, database, connectivity, and embedded systems. Develops AI- and LLM-integrated automation workflows, performs endurance and stability testing, validates releases, manages defects, repairs complex scripts, and conducts peer reviews. Partners with software engineers, reports results to leadership, supports risk-based testing and release readiness, and may lead projects or teams in a global delivery environment.

Designs and supports data platform blueprints, semantic models, ontologies, taxonomies, metadata schemas, and knowledge graphs. Collaborates with integration and data architects to integrate systems and data models. Develops scalable linked-data architectures using RDF, OWL, SKOS, and SHACL, and works with graph databases and query languages. Supports semantic search, data extraction, AI-driven recommendations, and data discovery while communicating complex concepts to technical and non-technical stakeholders.

Perform independent second-line technology risk testing across IT, security, cloud, and AI/ML domains. Execute end-to-end control testing, document findings (risk and control matrices), and work with business and risk teams to remediate gaps and report significant risks to management.

Design, develop and automate end-to-end test suites for cloud-based networking products. Execute functional, integration, performance and scale tests; review customer issues; collaborate with product, development and CloudOps; perform API and UI automation; and validate networking, security and virtualization features.

Lead global process transformation initiatives by redesigning workflows, mapping current and future processes, identifying offshoring and AI/RPA automation opportunities, developing ROI-based business cases, and managing end-to-end implementations. Responsibilities include KPI and service-level reviews, governance improvement, stakeholder workshops, and cross-functional project leadership within Ford Credit.

Lead AI opportunity discovery, prioritize use cases, design AI-enabled product strategies and implementation roadmaps. Translate operational challenges into feasible AI solutions, assess data readiness, define high-level architectures, build ROI models, develop governance for responsible AI, and drive stakeholder engagement and field-based implementation (50–80% travel).

Develop and deploy machine learning, statistical, and Generative AI solutions that address business challenges. Responsibilities include analyzing structured and unstructured data, building predictive models, creating RAG and agentic systems, developing data pipelines, managing model lifecycles, ensuring data governance and responsible AI, and communicating insights to technical and business stakeholders.

Leads a 60+ engineer APAC R&D organization developing industrial control products and integrated automation systems. Responsibilities include strategic planning, team leadership, software and controls development oversight, OT cybersecurity, validation, project budgets, stakeholder engagement, regulatory compliance, certifications, and continuous improvement. The role requires extensive automation and controls experience, engineering leadership, knowledge of FPGA systems, Windows software, Agile programs, IEC standards, cybersecurity, and complex product development.

Develop and apply machine learning innovations, primarily for NLP, to large-scale business problems. Conduct feasibility studies, analyze data, evaluate real-world applicability, and build scalable, maintainable, reproducible ML solutions. Collaborate on deployment and integration, communicate findings to technical and nontechnical stakeholders, understand business requirements, and contribute to team development and collaboration.

Leads enterprise technical delivery and software engineering strategy for US healthcare and payer analytics solutions. Oversees large-scale engineering teams, project lifecycles, budgets, quality, compliance, interoperability, and continuous improvement. Develops delivery roadmaps, manages risks and resources, mentors leaders, and partners with executives, clients, vendors, sales, marketing, and regulatory stakeholders. Requires deep healthcare domain knowledge, strong technical architecture expertise, and experience scaling organizations and converting opportunities into revenue.

Lead algorithm design and development for wafer inspection products, delivering image processing, computer vision, and machine learning solutions. Define performance requirements, mentor engineers, support system integration, validate and deploy algorithms, and ensure software quality and reliability.

Leads a global portfolio of process and analytics initiatives across pharmaceutical operations. Owns opportunity prioritization, value realization, project delivery, governance, risk management, stakeholder alignment, change adoption, and talent coaching. Uses process mining, automation, AI, and advanced analytics across SAP, Veeva, and Salesforce to improve efficiency, reduce risk, and deliver measurable financial and operational outcomes.

Lead KONE’s global People Analytics domain by defining its strategy, operating model, technology roadmap, and governance. Own analytics solutions in partnership with HR, IT, and business stakeholders; represent People & Communications in data governance forums; and support training, data literacy, and standardized people-data practices worldwide. The role requires leadership across business and technical teams, knowledge of HR analytics technologies such as Workday and Power BI, and understanding of AI, machine learning, automation, cloud infrastructure, and data architecture.

Lead enterprise data, analytics, and AI/ML strategy for a pharmaceutical organization. Drive cloud-based data platform design, data governance, BI and advanced analytics, operationalize ML models, and enable Commercial and R&D analytics using syndicated and clinical datasets.

Lead execution of post-sales product initiatives: roadmap planning, discovery, PRDs, AI/workflow automation, engineering collaboration, KPI dashboards, stakeholder alignment, launch enablement, and platform/backlog management.

Develop AI products using Python, Spring/Java, React, Azure, and Optum AI platforms. Lead a team of engineers delivering AI solutions, support production deployments, and apply expertise in AI, cloud, frontend, backend, and database technologies. The role requires a graduate degree or equivalent experience, at least nine years of relevant experience, and advanced knowledge of Azure, React, Java, Oracle, and MySQL. Relevant technology certifications are preferred.

Design, build, and automate secure AWS infrastructure and CI/CD pipelines. Ensure availability, performance, scalability, and compliance (PCI‑DSS, HIPAA, SOX, GDPR, CCPA). Manage releases/configuration, container and API security, serverless solutions, security automation/ML, monitor systems, and publish CICD best practices.

Design, build, and maintain backend APIs and services for a real-time supply chain visibility platform. Own system architecture, real-time event ingestion, data normalization, ML integration, monitoring, and production debugging while collaborating with Product and Data Science to deliver scalable, high-availability features.