- Design and deploy multi-agent orchestration platforms for manufacturing automation and business process automation using frameworks such as LangGraph, CrewAI, and LangChain.

- Build enterprise LLM integrations including RAG pipelines, domain-specific knowledge bases, and fine-tuning workflows tailored to client-specific domains and data.

- Develop computer vision pipelines for manufacturing use cases including quality control, process monitoring, and assembly line digitalization using YOLO, vision transformers, and multimodal LLMs.

- Automate internal business workflows across Finance, HR, Compliance, and Operations by designing and integrating AI-driven automation solutions.

- Establish and maintain AI governance frameworks covering model evaluation, prompt management, risk assessment, and responsible AI practices at enterprise scale.

- Collaborate with data engineers, platform architects, and business stakeholders to define AI system requirements and translate them into production-ready ML solutions.

- Integrate vector databases, embedding models, and MLOps tooling to support scalable model deployment, monitoring, and lifecycle management.

- Conduct model evaluation, red-teaming, and performance benchmarking; iterate on prompts, architectures, and fine-tuning strategies to meet quality thresholds.

- Stay current with advances in LLMs, agentic AI, and Industry 4.0 technologies; contribute to internal knowledge sharing and technical community.

Required Qualifications & Skills :

- B.E. / B.Tech / M.Tech / M.Sc. in Computer Science, Artificial Intelligence, Data Science, or a related discipline.

- 7+ years in AI/ML, with at least 2+ years of hands-on experience in LLMs and generative AI systems in production.

- LLMs & GenAI : Demonstrated expertise in RAG pipeline design, prompt engineering, domain-specific fine-tuning, and enterprise LLM integration (OpenAI, Anthropic, Mistral, open-source models).

- Agentic AI : Hands-on experience building and deploying multi-agent systems using LangGraph, LangChain, CrewAI, or equivalent frameworks.

- Programming & ML : Strong Python proficiency; expertise with ML frameworks (PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers) and MLOps tooling.

- Strong problem-solving and communication skills; ability to work across technical and business teams and present complex AI concepts clearly to non-technical stakeholders.

- AI Governance : Experience establishing model evaluation frameworks, guardrails, risk tiering, and responsible AI practices at enterprise scale.

- Vector Databases & MLOps : Familiarity with Pinecone, Weaviate, ChromaDB, or similar; experience with MLflow, Weights & Biases, or equivalent MLOps platforms.