Gen AI Engineer | (7+ Years Experience)|Hybrid working |Pune,India

Job Summary :

We are seeking an experienced Generative & Agentic AI Developer with 7+ years of IT experience, including strong expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) and agent-based AI systems. This role focuses on developing and deploying advanced GenAI applications and autonomous Agentic AI workflows that enable intelligent automation, reasoning, and decision-making. The ideal candidate will have hands-on proficiency with Python, AI / ML frameworks, multi-agent orchestration platforms, prompt engineering and cloud-native environments to ensure scalable, secure, and production-ready solutions.

Job Type : Hybrid, Full-Time

Primary Work Location :  Pune

In this Role, Your Responsibilities Will Be :

• Design, fine-tune, and deploy advanced GenAI models (LLMs, Diffusion, GANs, VAEs, Transformers, GNNs) for multimodal, NLP, and autonomous agentic applications.
• Engineer end-to-end RAG pipelines and multi-agent workflows using LangChain, AutoGen, Copilot, MCP.
• Integrate vector DBs (PostreSQL, Pinecone, Weaviate, Chroma, FAISS, Redis) and graph DBs (Neo4j, Postgres / pgvector) for semantic retrieval and contextual reasoning.
• Optimize foundation models (GPT-4, LLaMA, Mistral, Falcon) via prompt engineering, RLHF, LoRA, quantization, and hyperparameter tuning.
• Build scalable AI solutions using Azure AI / ML (preferred) with containerized deployments (Docker, Kubernetes).
• Apply MLOps best practices : CI / CD, model versioning, drift detection, observability, and lifecycle management with MLflow, Kubeflow, Airflow, and monitoring tools.
• Develop secure AI pipelines and APIs with Python, FastAPI / Flask, RBAC, OAuth2, JWT, and encryption standards.
• Conduct model optimization : prompt engineering , hyperparameter tuning, cross-validation, and performance monitoring.
• Use tools like Azure Machine Learning , OpenAI API, HuggingFace, or custom PyTorch / TensorFlow-based models.
• Implement AI safety, bias mitigation, interpretability (SHAP, LIME, and compliance guardrails (GDPR, HIPAA, ISO).
• Collaborate with cross-functional teams to deliver enterprise-grade copilots, assistants, and reusable AI components.
• Document AI design, model workflows, and deployment pipelines for audit readiness and knowledge sharing.
• Build scalable AI applications using MLOps, CICD, containerization (Docker, Kubernetes) and deploy on Azure AI Infrastructure (preferred) or AWS if needed.

Qualifications :

Preferred Qualifications :