Top Machine Learning Jobs in Chennai

Owns the strategy, roadmap, delivery, and adoption of enterprise AI enablement platforms and AI products. Responsibilities include defining AI capabilities, prioritizing use cases, managing product lifecycles, partnering with engineering and data teams, establishing responsible AI governance, managing budgets and vendors, and influencing senior stakeholders. The role also mentors product teams and drives AI adoption, evaluation, monitoring, compliance, and organizational transformation.

Leads software and AI/ML engineering strategy, architecture, delivery, and organizational development. Manages multi-team engineering organizations, roadmaps, budgets, hiring, and leadership development while overseeing cloud-native services, APIs, machine learning, generative AI, RAG, AI agents, MLOps, LLMOps, responsible AI, security, reliability, and production operations. Partners with product, data, security, and business leaders to deliver measurable outcomes and accelerate modernization.

Build and scale machine learning systems for search, including ranking models, feature engineering, model deployment, monitoring, experimentation, and online inference. Design fault-tolerant distributed infrastructure, optimize search latency and throughput, and develop reliable ML services from proof of concept through production. Collaborate across engineering and product teams, communicate technical decisions, review code, document systems, and mentor junior engineers.

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.

Lead the ethical use and governance of AI, ensuring compliance with legal standards, managing AI risks, and fostering collaboration across teams.

Leads AstraZeneca’s enterprise Data and AI forward engineering strategy across Finance, HR, Legal, Compliance, M&A and other enabling functions. Builds data products, AI and agentic solutions, engineering capabilities, governance and responsible AI practices. Connects experimentation to measurable business value, develops AI skills, manages distributed teams and strategic partners, and improves operational resilience, automation and FinOps. The role also shapes product-focused operating models, talent development and adoption of AI across India and the UK.

Leads AstraZeneca’s enterprise Data and AI forward-engineering strategy across Finance, HR, Legal, Compliance, M&A and other enabling functions. Builds data products, AI agents, engineering capabilities and product-focused teams; establishes governance, security, privacy, responsible AI and value measurement practices; scales experimentation into measurable business outcomes; develops talent across India and the UK; and improves managed-service resilience, automation and cost efficiency.

Design and implement production-ready AI and machine learning applications using generative AI, LLMs, deep learning, neural networks, chatbots, and image processing. Deploy scalable solutions across cloud and on-premises infrastructure, optimize ML workflows, collaborate with stakeholders, make technical decisions, and mentor junior engineers.

Leads enterprise knowledge engineering and AI initiatives, designing ontologies, knowledge graphs, semantic layers, and graph-based machine learning solutions. Owns architecture and delivery for complex client programs, develops teams, drives innovation and thought leadership, and advises senior stakeholders. Responsibilities include integrating knowledge systems with data platforms, building AI and LLM pipelines, applying NLP and search techniques, shaping sales and pre-sales efforts, and establishing standards for scalable knowledge graph solutions.

Leads enterprise knowledge engineering and knowledge graph initiatives, designing ontologies, semantic models, AI solutions, and data pipelines. Owns architecture and delivery for complex client programs, applies graph-based machine learning and LLM technologies, and establishes standards for knowledge model governance. Builds executive stakeholder relationships, supports sales and presales, develops reusable intellectual property, leads teams, mentors engineers, and drives innovation across AI, semantic technologies, and agentic systems.

Architect and optimize production AI and Snowflake infrastructure, including scalable warehouses, Snowpark workloads, secure data and feature pipelines, model enablement, automation, CI/CD, observability, governance, and cost management. Evaluate infrastructure and technology options, establish architecture standards and roadmaps, integrate AI/ML systems into enterprise environments, lead technical workstreams, mentor engineers, and ensure performance, security, compliance, reliability, and scalability.

Architects and engineers scalable Snowflake-based AI and data infrastructure for production machine learning applications. Designs warehouses, Snowpark workloads, feature pipelines, model integrations, governance, CI/CD, observability, security, and cost controls. Evaluates architecture alternatives, leads assessments, defines roadmaps, and optimizes performance and reliability. Collaborates with clients and engineering teams, documents architecture decisions, establishes standards, and mentors engineers across AI infrastructure and MLOps initiatives.

Architects and builds scalable Snowflake-based AI and machine-learning infrastructure, including warehouses, Snowpark workloads, feature pipelines, model integrations, automation, governance, monitoring, and CI/CD. Optimizes performance, cost, security, reliability, and scalability while evaluating technologies and vendors. Leads architecture reviews, roadmaps, capacity planning, and production enablement; collaborates with clients and engineering teams, documents architectural decisions, and mentors engineers across AI infrastructure initiatives.

Leads the architecture, development, and production delivery of AI/ML capabilities in enterprise healthcare applications. Builds ML and generative AI solutions, RAG systems, model-serving APIs, MLOps workflows, distributed cloud services, and CI/CD pipelines. Ensures responsible AI, security, privacy, reliability, monitoring, and operational readiness. Influences architecture across teams, mentors engineers, documents technical decisions, and delivers reusable production components.

Leads mainframe modernization and migration initiatives for large global clients. Develops transformation and re-platforming solutions, conducts discovery workshops, presents technical and business cases, advises executives, and partners with sales, consulting, operations, and product teams. Requires extensive mainframe expertise across legacy technologies, migration architecture, data modernization, cloud and hybrid environments, and enterprise hosting. The role also supports AI-driven modernization, technical documentation, project lifecycle management, and client relationship development.

Lead enterprise detection engineering across SIEM, EDR, NDR, cloud, on-premises, and OT/ICS environments. Own detection strategy and content mapped to MITRE ATT&CK, optimize queries and reduce false positives, automate detection-as-code workflows, and partner with SOC, incident response, threat intelligence, and purple teams. Establish quality standards, validate coverage through adversary emulation, integrate threat intelligence, and lead platform and vendor collaboration.

Leads enterprise detection engineering across SIEM, EDR, NDR, cloud, on-premises, and OT/ICS environments. Owns detection strategy, content development, MITRE ATT&CK coverage, platform tuning, automation, detection-as-code, quality standards, and performance metrics. Partners with SOC, incident response, threat intelligence, offensive security, engineering teams, and vendors to validate detections, reduce false positives, close coverage gaps, and improve time-to-detect.

Client-facing healthcare analytics business analyst responsible for engaging senior executives, identifying business pain points, designing demonstrable analytics solutions, supporting consultative sales, and helping shape product roadmaps. The role requires deep U.S. healthcare knowledge, claims and interoperability expertise, hands-on analytics experience, stakeholder communication, requirements documentation, product collaboration, and mentoring. Work spans healthcare data management, BI, cloud, AI, machine learning, and emerging analytics services.

Lead end-to-end data science solutions for cyber fraud prevention, including machine learning models, anomaly detection, risk scoring, feature engineering, data pipelines, APIs, automated decisioning, and production monitoring. The role requires advanced Python and SQL, cloud and deployment experience, model validation, operational support, technical documentation, vendor evaluations, cross-functional collaboration, and mentoring of less-experienced team members.

Develops scalable automated testing frameworks and test suites for cloud and big-data platforms. Responsibilities include Python/PySpark automation, SQL and service testing, manual and performance testing, CI/CD deployment, monitoring utilities, integration testing, and GenAI automation. The role collaborates with development, infrastructure, operations, and client teams while providing technical guidance and maintaining test documentation.

Design, develop, deploy, and integrate AI, machine learning, and generative AI solutions for industrial and business applications. Own the machine learning lifecycle, define architectures, assess data readiness, monitor production models, and ensure reliability and maintainability. Evaluate emerging AI technologies, manage technical delivery from partners and contractors, and collaborate with cross-functional stakeholders to identify use cases, support adoption, and deliver measurable operational improvements.

Designs, develops, deploys, and integrates AI, machine learning, deep learning, and generative AI solutions for industrial and business applications. Owns the machine learning lifecycle, solution architecture, data strategy, model monitoring, and production integration. Evaluates emerging technologies, manages technical delivery from partners and contractors, and collaborates with cross-functional teams to deliver scalable, reliable, and maintainable AI solutions.

Designs, develops, deploys, and integrates AI, machine learning, deep learning, and generative AI solutions for industrial and business challenges. Responsibilities include selecting modeling approaches, managing the ML lifecycle, defining solution architectures, analyzing operational data, supporting production deployment and monitoring, managing technical partners, and ensuring data governance, reliability, and maintainability. The role also evaluates emerging technologies, collaborates across functions, and supports user adoption and continuous innovation.

Designs, develops, deploys, and integrates AI, machine learning, generative AI, and analytics solutions for industrial and business challenges. Owns the machine learning lifecycle, solution architecture, data strategy, production integration, monitoring, and technical evaluation of internal and external partners. Collaborates with cross-functional stakeholders, supports adoption, evaluates emerging AI technologies, and develops solutions such as predictive maintenance, anomaly detection, forecasting, document analysis, and knowledge tools.

Evaluate scientific manuscripts in computer science, engineering, data science, machine learning, and related fields; coordinate peer review; provide author feedback; engage researchers through outreach and conferences; recruit high-quality submissions; strengthen journal visibility; and contribute to editorial strategy, journal development, and broader science communication initiatives.