Top Tech Jobs & Startup Jobs in Chennai

Develops end-to-end generative AI and NLP solutions using LLMs, including chatbots, search, summarization, Document AI, and content generation. Conducts research, builds supporting libraries and frameworks, and applies prompt engineering, fine-tuning, vector databases, and cloud AI services. May also work with computer vision, audio, and video models. Collaborates cross-functionally throughout the software development lifecycle, contributing to design, testing, deployment, maintenance, code reviews, and technical roadmaps.

Design and implement end-to-end generative AI and LLM solutions, including NLP, computer vision, audio, and video applications. Research emerging AI techniques, develop supporting libraries and frameworks, and apply prompt engineering, fine-tuning, vector databases, and cloud AI services. Participate in software development, testing, deployment, maintenance, and code reviews while collaborating with cross-functional teams.

Designs and implements end-to-end generative AI and LLM solutions, researches emerging AI techniques, develops supporting libraries and frameworks, and participates in software delivery from design through maintenance. The role applies NLP, computer vision, audio, and multimodal AI methods; uses cloud and open-source platforms; contributes to code reviews and roadmaps; and collaborates with cross-functional teams.

Owns technical direction for enterprise agentic AI solutions across application, cloud, data, and AI layers. Leads architecture decisions, deployment standards, security reviews, reusable infrastructure, CI/CD practices, and production monitoring. Mentors Forward Deployed Engineers toward independent delivery, guides design and code reviews, prototypes high-risk components, and resolves critical blockers. The role emphasizes technical leadership, scalable deployment capability, cloud cost discipline, agent evaluation, guardrails, and operational reliability rather than sustained feature development.

The Senior Azure Data Engineer will design, develop, and maintain data infrastructure and pipelines, ensuring data quality, integrity, and alignment with business objectives. Responsibilities include collaborating with teams and establishing best practices for data engineering.

Design, build, and maintain Azure-based data infrastructure and pipelines. Implement data models and schemas, ensure data quality, optimize distributed storage and processing (Databricks/PySpark), and establish engineering best practices while partnering with cross-functional teams.

Work with enterprise customers to design, build, and deploy production-grade AI solutions on the Cogentiq platform. Implement integrations with enterprise systems, develop connectors, backend services, and microservices, ensure scalability, security and observability, troubleshoot deployments, and turn deployment learnings into reusable platform capabilities.

Design, implement, and manage GCP-based CI/CD pipelines and platform infrastructure. Automate provisioning with IaC (Terraform/ARM), maintain build/release pipelines, manage Git workflows, integrate automated testing, enforce security best practices, and implement monitoring, logging, and tracing. Collaborate with development and security teams to ensure reliable deployments and platform health.

The role involves leading Azure architecture engagements, designing cloud-native solutions, mentoring teams, and collaborating with various stakeholders to optimize cloud adoption and security.

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.