Lead Developer - GenAI, Agentic AI

Greetings from BMW Techworks India Pvt Ltd!!!

Role: Lead Developer - GenAI, Agentic AI

Location: Chennai

Experience: 8 to 10 Years

Notice period: We are looking for immediate joiners or candidates with a maximum notice period of 30 days.

What awaits you/ Job Profile?

We are looking for a highly skilled and hands-on Lead Developer to drive the design and development of next-generation AI, GenAI, and Agentic AI solutions. This role requires strong technical leadership combined with deep software engineering expertise to build scalable, enterprise-grade AI platforms and applications.

You will work closely with Product Owners, Architects, Business Stakeholders, and Engineering teams to translate business problems into innovative AI-powered solutions. The ideal candidate should be capable of developing end-to-end applications, leading architecture discussions, guiding engineering teams, performing code and design reviews, and ensuring delivery excellence across the software development lifecycle.

• Develop AI/GenAI/Agentic AI applications.
• Design scalable, cloud-native, microservices-based AI platforms and solutions.
• Provide technical leadership to development teams through mentoring, code reviews, and architecture governance.
• Collaborate with Product Owners and business stakeholders to understand requirements and convert them into technical solutions.
• Drive AI solution design using LLMs, RAG, AI Agents, orchestration frameworks, and enterprise integration patterns.
• Establish engineering best practices, coding standards, CI/CD pipelines, observability, and deployment strategies.
• Review system architecture, security, scalability, performance, and maintainability aspects of applications.
• Support troubleshooting, technical clarifications, and solution optimization across teams.
• Evaluate emerging AI technologies, frameworks, and tools for enterprise adoption.
• Ensure high-quality software delivery with strong focus on reliability, security, and operational excellence.

What should you bring along?

• Strong hands-on software engineering experience with proven expertise in building enterprise-scale applications.
• Experience leading engineering teams in Agile/Scrum environments.
• Excellent architectural thinking and problem-solving capability.
• Strong communication and stakeholder management skills.
• Ability to work closely with cross-functional teams including Product, Engineering, Data Science, and Business teams.
• Passion for AI innovation and continuous learning in the rapidly evolving AI ecosystem.
• Ability to balance hands-on coding with technical leadership responsibilities.
• Experience in designing secure, scalable, and production-grade AI systems.

Must have technical skill:

• Strong programming expertise in Python and frontend technologies 
• Hands-on experience with Generative AI, LLMs, and Agentic AI application development.
• Experience with frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or Semantic Kernel.
• Strong understanding of Retrieval-Augmented Generation (RAG), embeddings, vector databases, prompt engineering, and AI orchestration.
• Experience integrating enterprise AI solutions with OpenAI, Azure OpenAI, Anthropic, Gemini, or open-source LLMs.
• Strong expertise in cloud-native application development on AWS, Azure, or GCP.
• Experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Deep understanding of microservices architecture and API-driven development.
• Hands-on experience with REST APIs, event-driven architecture, and asynchronous systems.
• Experience with DevOps, CI/CD pipelines, Infrastructure as Code, and automated deployments.
• Strong knowledge of software engineering best practices, design patterns, clean architecture, and secure coding principles.
• Experience with Git, code review practices, automated testing, and engineering governance.
• Knowledge of observability, logging, monitoring, and production support practices.

Good to have technical skills:

• Experience building multi-agent AI systems and autonomous workflow orchestration.
• Exposure to AI governance, responsible AI, model evaluation, and AI security practices.
• Experience with MLOps platforms and model lifecycle management.
• Familiarity with vector databases such as Pinecone, Weaviate, ChromaDB, Milvus, or FAISS.
• Experience with streaming technologies such as Kafka or RabbitMQ.
• Exposure to Graph databases and Knowledge Graph implementations.
• Experience with frontend frameworks such as React or Next.js for AI-powered applications.
• Familiarity with data engineering and big data ecosystems.
• Experience in enterprise architecture reviews and technology consulting.
• Relevant cloud and AI certifications will be an added advantage.