Top Tech Jobs & Startup Jobs in Chennai

Build and manage Azure Databricks jobs, lakehouse pipelines, reusable Python/PySpark scripts, and cloud-native microservices. Develop LLM-powered copilots, agentic workflows, RAG pipelines, prompt strategies, guardrails, and AI inference services. Integrate enterprise data with AI solutions, optimize performance and costs, implement governance and observability, and contribute to architecture and technical design. The role also involves independent ownership, code reviews, mentoring, stakeholder collaboration, and delivering production-grade AI solutions.

Supports end-to-end procurement operations, including requisitions, RFQs, purchase orders, order tracking, vendor management, and invoice monitoring. Analyzes spend, savings, vendor performance, and procurement KPIs using reports and dashboards. Maintains procurement and vendor data, supports negotiations and contract renewals, conducts price benchmarking, and identifies cost-saving, consolidation, and process-standardization opportunities.

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.

Build and own production-grade AI systems, including backend services, scalable APIs, LLM integrations, RAG pipelines, and agentic workflows. Responsibilities include testing, documentation, model deployment, performance optimization, collaboration, and continuous learning, with increasing ownership and complexity based on experience.

Leads the design, development, and delivery of scalable enterprise full-stack applications using Python, FastAPI, Java, Spring Boot, React, Angular, mobile technologies, and Azure. Builds APIs and microservices, supports CI/CD, contributes to architecture and roadmaps, troubleshoots production issues, and ensures secure, maintainable performance. The role also leads code reviews, collaborates with stakeholders, documents technical solutions, and mentors distributed engineering teams.

Drive AI research strategy and build scalable NLP and Generative AI solutions. Develop prompt engineering, fine-tuning, RAG, model evaluation, experimentation, and optimization pipelines while collaborating with AI Engineering teams. The role emphasizes scientific thinking, reproducibility, ownership, technical leadership, continuous innovation, and progressively increasing responsibility.

Architect scalable, low-latency AI/ML systems and real-time inference pipelines for voice, vision, and NLP applications. Lead model productionization, infrastructure and framework selection, architecture reviews, optimization, observability, and MLOps best practices. Collaborate with product, data science, and engineering teams while mentoring engineers. The role requires expertise in distributed systems, LLM inference optimization, vector databases, RAG, containerization, cloud platforms, CI/CD, edge deployment, and software architecture.

Develop and improve NLP and generative AI solutions using prompt engineering, fine-tuning, retrieval-augmented generation, dataset preparation, model evaluation, experimentation, and optimization. Collaborate with AI Engineering teams, promote reproducibility, and progressively take on greater ownership while supporting continuous innovation.

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.

Develop and improve NLP and generative AI solutions through prompt engineering, model fine-tuning, RAG pipelines, evaluation, experimentation, and optimization. Collaborate with AI Engineering teams, promote reproducibility, and progressively take on greater ownership while driving continuous innovation.