Top Machine Learning Jobs in Chennai

Lead technical support for B2B customer deployments, diagnosing issues across ML models, applications, APIs, infrastructure, networking, and deployment environments. Investigate incidents, review logs and configurations, coordinate escalations with FDE, SRE, and Engineering teams, monitor deployment health, document root causes, communicate with customer stakeholders, and develop tools and dashboards to meet support SLOs.

Build foundational AI and machine learning capabilities for Twilio’s Customer Memory platform. Responsibilities include researching LLM orchestration, retrieval, personalization, and autonomous agents; designing rigorous experiments; and deploying production-grade ML systems at scale. The role requires end-to-end ownership, cloud-based services, high-volume or real-time inference, technical leadership, mentorship, and collaboration across globally distributed teams.

Lead the architecture and development of scalable AI infrastructure, distributed microservices, real-time data pipelines, and agentic AI systems for intelligent networking. Drive the full software lifecycle from design through production operations, establish technical direction, mentor engineers, and improve system reliability, security, observability, performance, and scalability.

Lead the technical vision, architecture, development, testing, deployment, and operations of scalable AI infrastructure, distributed microservices, real-time data platforms, and agentic AI systems. Build secure, resilient, observable services processing high-volume network data; guide technical strategy, improve engineering practices, and mentor engineers. Translate complex technical challenges into production-ready solutions across intelligent networking, cloud platforms, analytics, and automation.

Lead the architecture, development, testing, deployment, and operation of scalable AI infrastructure, distributed microservices, real-time data platforms, and agentic AI systems. Define technical vision, build secure and observable high-performance services, process large-scale network data, and integrate intelligent networking capabilities. Mentor engineers, lead technical discussions and reviews, improve system reliability and performance, and translate complex technical challenges into production-ready solutions.

Lead the architecture and development of scalable, secure, observable AI infrastructure, distributed microservices, real-time data pipelines, and agentic systems for intelligent networking. Drive technical vision, software delivery, system reliability, and operational excellence while mentoring engineers. Build production platforms processing high-volume network and operational data, integrating cloud infrastructure, machine learning, telemetry, and automation capabilities.

This is an incomplete draft job posting for Poshmark. It provides company background but does not identify the team, role purpose, responsibilities, qualifications, technologies, compensation, location, or travel requirements.

Drive full-cycle sales for complex, high-value AI research workspace opportunities. Originate and close new business, support strategic renewals, expand stakeholder relationships, lead value-based workshops, and translate AI and research analytics capabilities into measurable customer outcomes. Engage senior decision-makers across enterprise, government, academic, and research organizations while developing pipelines, territories, business cases, negotiations, and long-term account growth.

Owns the full sales cycle for complex, high-value AI research workspace opportunities, from prospecting and discovery through negotiation and closure. Builds executive stakeholder relationships, generates new business, supports strategic renewals, leads workshops, develops business cases, and communicates measurable value from AI-powered research and knowledge workflow solutions. Requires enterprise sales expertise, AI fluency, consultative selling skills, and extensive regional travel.

Implement enterprise ML and GenAI solutions in production, including AI agents, LLM automation, RAG, knowledge grounding, and industrialized processing pipelines. Establish ML lifecycle, MLOps, and LLMOps best practices; integrate Microsoft AI and Power Platform services; support deployments across environments; gather requirements; estimate work; present solutions; and document technical implementations.

Designs, experiments with, deploys, monitors, and evaluates machine learning models. Requires full-stack development involving Python, React, AWS, APIs, and SQL, along with statistical and probabilistic foundations. The role supports AI/ML initiatives in a consulting environment serving financial services and energy clients.

Leads data engineering initiatives, owning data models and warehouse solutions across SAP HANA, Datasphere, and public cloud platforms. Designs and operates data ingestion, integration, modeling, and analytics solutions from disparate enterprise and third-party sources. Provides technical leadership to distributed engineering teams, establishes standards, supports data science and business teams, and ensures scalable, reliable, secure infrastructure. Collaborates with stakeholders across sales, marketing, credit, finance, IT, and engineering.

Drive adoption of Databricks Genie across a regional territory through technical strategy, executive engagement, workshops, presentations, demos, and customer-facing collateral. Partner with Account Executives, GTM, Product, Engineering, GSIs, and consulting firms. Architect governed analytics solutions spanning semantic models, warehouses, metric layers, Lakehouse, and AI ecosystems; address governance and security concerns; resolve complex technical escalations; and provide roadmap feedback.

Lead data science initiatives for Quvia’s connectivity platform by developing predictive, prescriptive, forecasting, classification, and optimization models. Integrate anomaly detection, knowledge graphs, neural networks, and generative AI into products; deploy and continuously refine production-ready models; establish data quality and governance standards; document workflows; and mentor junior data scientists while collaborating cross-functionally with engineering and product teams.

Lead product strategy and roadmap for AI-powered Digital Assistant across quote-to-cash workflows. Translate business needs into product requirements, collaborate with data scientists, ML engineers, designers and software teams, prioritize features, monitor metrics, ensure ethical AI use, drive execution from ideation to launch, and communicate with stakeholders through demos and training to improve support efficiency and customer satisfaction.

Lead product strategy and roadmap for AI-powered Digital Assistant across quote-to-cash workflows. Translate business needs into requirements, collaborate with ML and engineering teams, prioritize features, monitor performance, and ensure ethical AI. Drive cross-functional alignment, build business cases, and deliver demos to stakeholders.

Lead strategy, vision, development, and growth for an agentic AI platform that automates complex enterprise tasks. Responsibilities include customer and market discovery, competitor analysis, feature prioritization, backlog and roadmap management, agile delivery, and collaboration with engineering, AI/ML, design, sales, marketing, and solutions teams. The role also supports product demos, enablement, client presentations, adoption, and revenue growth.

Architect and oversee scalable, real-time software platforms for engineering simulations and operational mobility tools. Define system architecture, module boundaries, APIs, data objects, execution models, and integration traceability. Lead platforms from prototype through production, write low-level requirements, establish development and testing standards, and support ML and GenAI implementations. Analyze timing, CPU, memory, and distributed-system performance while collaborating with technical teams throughout proof-of-concept, deployment, and optimization phases.

Lead the technical roadmap, architecture, development, evaluation, and productionization of machine learning and AI solutions for search relevance. Build models using queries, product catalogs, and behavioral data; integrate scalable ML components into cloud systems; analyze search quality; mentor engineers; and influence cross-functional engineering and product decisions.

Own end-to-end machine learning for ecommerce discovery, including recommendation and ranking models, low-latency Python serving, batch pipelines, A/B testing, and production deployments. Operate containerized services on Kubernetes, monitor systems, and extend LLM-powered shopping and catalog applications. The role requires strong recommender systems, search ranking, embeddings, vector databases, streaming, and production ML experience.

Lead development and innovation of managed switching solutions targeting SMEs, focusing on product features, cost-performance, technical architecture, and market-driven solutions.

Design and implement a unified orchestration layer coordinating vendor and internal LLMs and human agents. Build production-grade Python services, drive ML projects from design to delivery, ensure secure, scalable AI-enabled customer experiences, and collaborate across teams to meet security, compliance, and performance standards.

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.

Develop, train, deploy, and maintain machine learning and generative AI solutions, including predictive models, NLP applications, chatbots, LLMs, and agentic AI systems. Design scalable ETL pipelines, optimize databases, process large datasets with Spark and Snowflake, and create business visualizations. The role requires expertise across Python, SQL, cloud AI platforms, machine learning frameworks, databases, and data engineering.

Develop and deploy machine learning tools for engineering simulation applications, including surrogate models, data pipelines, chained model systems, and retraining workflows. Audit simulation data, prototype and productize research, benchmark model performance, establish evaluation methodologies, and deploy AI tools to engineering teams. The role requires deep learning experience with scientific or engineering data, strong Python ML libraries, surrogate modeling, and database and retrieval pipeline skills.