Top Machine Learning Jobs in Chennai

Owns the end-to-end product direction and quality of AI-powered systems, translating model capabilities, evaluations, and engineering constraints into requirements and priorities. Partners closely with ML, backend, and client engineers on system design, experimentation, evaluation frameworks, reliability, safety, latency, cost, and user experience. Identifies failure modes, establishes quality standards, drives rapid iteration, and ensures AI features are useful, predictable, understandable, and trusted.

Leads the company’s machine learning research and development strategy, focusing on multimodal generative models for video, audio, and language. Designs architectures and large-scale training pipelines, builds and mentors a high-performing ML team, and drives deployment of research innovations into production. Collaborates with product, engineering, and creative teams, establishes ML standards, evaluates emerging technologies, and represents the company through research publications and conferences.

Design and build scalable data and AI infrastructure for production LLM applications. Responsibilities include developing RAG systems, embedding and vector retrieval pipelines, AI agents, LLM evaluation and observability frameworks, and batch or streaming data platforms using Databricks, Spark, Snowflake, Delta Lake, and Airflow. The role also involves optimizing distributed workloads, establishing data governance and quality practices, and partnering with engineering and product teams to productionize AI capabilities.

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.

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.

Analyze large datasets, build and evaluate machine learning models, prepare and transform data, and create Power BI dashboards. Apply statistical methods and A/B testing, use cloud platforms (AWS, Snowflake), leverage Generative AI/LLMs, ensure data governance and responsible AI, and present insights while collaborating in an Agile team.

Design, build, and deploy conversational AI/chatbot solutions on the Kore.AI platform. Create intents, entities, dialog flows, integrate backend systems and REST APIs, and develop responsive user interfaces with ReactJS while collaborating with UI/UX designers.

Design and implement machine learning and NLP solutions, build prototypes and demonstrations, work with LLMs and deep learning frameworks, and leverage cloud services to productionize AI models.

Lead end-to-end AI solutions for clients, manage project delivery and people, partner with sales for business development, collaborate with engineering/MLOps/DevOps, ensure data quality, model training, tuning, deployment, monitoring, and provide executive updates on status and risks.

Evaluate and author high-quality computer science content to improve AI model reasoning and accuracy. Analyze complex CS problems, create scenarios/datasets, review AI outputs for correctness, and collaborate remotely to refine guidelines and documentation.

Lead implementation of AI and automation across legal functions: deploy contract intelligence, fraud detection, and KYC automation; digitize manual legal workflows and inbox triage; build self-service document tools; maintain the legal tech stack and KPIs; train legal teams to drive adoption. First-six-month goals include KYC digitization, AI-driven inbox triage, and a contract analysis tool that reduces review time by 30%+.

Build enterprise AI agents using Microsoft Copilot Studio and Azure AI Foundry. Develop topics, triggers, routing, fallback handling, generative answers, RAG, knowledge grounding, citations, metadata-based retrieval, multilingual capabilities, and prompts. Integrate agents with Power Automate, SharePoint, Dataverse, Teams, Azure AI Search, and Azure OpenAI or Foundry models. Support troubleshooting, security, environments, ALM, and DEV/UAT/PROD deployments.

Design, prototype, and deliver agentic AI solutions using ML and generative models. Partner with product, engineering, and delivery teams to build, evaluate, and deploy LLM-based solutions, run experiments and POCs, and communicate technical recommendations to stakeholders.

Evaluate AI-generated technical content, craft and answer advanced ML/AI prompts, assess model responses for accuracy, relevance, and clarity, identify errors or hallucinations, and provide feedback to improve AI systems.

Lead the product strategy and execution for an AI-first Email Marketing platform. Own Email Builder UX, AI-native content generation, Sequence AI, delivery/automation, experimentation, reporting, consent management, and cross-functional alignment to drive adoption, engagement, and revenue.

Own end-to-end enterprise sales across Southeast Asia: prospecting, partner-led growth, channel management, demos, negotiation, forecasting, and closing. Build territory and partner plans, hit revenue quotas, represent CloudSEK to CISOs, collaborate with engineering, marketing and customer success, and manage onboarding and contract negotiations.

Own the production lifecycle for traditional ML and generative AI systems, including deployment pipelines, model registries, monitoring, retraining, governance, and incident response. Build reliable AWS-based infrastructure for LLM and agentic applications, establish observability and cost controls, and create operational standards, runbooks, and documentation. Partner with U.S.-based Data Science, Data Engineering, and Product teams while independently managing production workloads during India operating hours.

Design, develop, and implement advanced statistical and machine learning algorithms. Analyze disparate data sources to discover insights, build predictive models, prototype solutions, and collaborate with engineers to productionize models. Communicate findings to business and IT stakeholders and define system and functional requirements.

Lead backend architecture and engineering: design scalable APIs and services, mentor engineers, write complex code, enforce best practices for databases, testing, observability, and integrate ML/LLM workloads.

Own data quality and root-cause analysis for prediction issues: detect anomalies, trace sources, fix processes, automate QA/reporting with Python and SQL, maintain dashboards, and partner with Data Science, Engineering, Product, and Commercial teams to improve prediction performance and customer-facing analytics.

Build governed Databricks Lakehouse foundations and productionize machine learning and GenAI solutions, including forecasting, anomaly detection, NLP, RAG, and predictive analytics. Develop reliable batch and streaming workflows, CI/CD, monitoring, governance, security, lineage, and semantic models. Collaborate with product, data, platform, and domain teams to deliver measurable customer value while meeting reliability, compliance, cost, and operational objectives.

Design and build machine learning systems for Coinbase, responsibly use generative AI tools and copilots, apply human-in-the-loop practices, and deliver measurable efficiency, cost, and quality improvements while collaborating in a remote-first environment with periodic in-person surges.

ValGenesis is hiring software engineers at all levels, focusing on collaboration and innovation. Roles require 2 to 12 years of experience across various engineering disciplines.

The Senior Data Analyst will leverage data to inform strategic decisions, analyze complex datasets, develop predictive models, and communicate insights to stakeholders.

Lead an RPA team to design, develop, test, deploy, and maintain scalable automation bots (UiPath/AA/Blue Prism). Collaborate with stakeholders to assess processes, manage projects, mentor developers, troubleshoot bots, and report progress to senior management.