Generative AI Engineer

Job Title

Location:

Experience:

Job Type:

Role Summary:

• Lead end-to-end development of Generative AI solutions using LLMs for enterprise use cases (chatbots, document intelligence, summarization, search, automation, etc.).
• Architect and implement GenAI applications using prompt engineering, RAG (Retrieval Augmented Generation), fine-tuning, and agent-based frameworks
  , and MCP (Model Context Protocol) for secure tool and data integration.
• Evaluate and integrate LLMs (OpenAI, Azure OpenAI, Anthropic, Google, open-source models like LLaMA, Mistral).
• Design scalable GenAI architectures including model orchestration, vector databases, embeddings, and APIs.
• Guide teams on best practices for prompt design, model evaluation, performance optimization, and cost control.
• Collaborate with business stakeholders to translate requirements into GenAI solutions.
• Ensure responsible AI practices including data privacy, security, bias mitigation, and governance.
• Own performance and value metrics for GenAI platforms, balancing model accuracy, response latency, and operational cost while ensuring alignment with business priorities.
• Mentor junior engineers and provide technical leadership in GenAI initiatives.
• Stay current with GenAI trends, tools, and frameworks and recommend adoption strategies.

Technical Skills(Mandatory):

• 6–8 years of overall experience with

  4+ years in AI/ML or GenAI-related roles

  .

• Strong hands-on experience with LLMs and GenAI frameworks (LangChain,Langraph, LlamaIndex, Semantic Kernel, CrewAI, etc.). – Must have

• Proficiency in Python with hands-on experience in deep learning frameworks (PyTorch or TensorFlow) and experience building production-grade APIs (FastAPI/Flask).

• Solid understanding of RAG pipelines, embeddings, vector databases (FAISS, Pinecone, Weaviate, Chroma). – Must have

• Experience with prompt engineering, model evaluation, and fine-tuning techniques.

• Working knowledge of cloud platforms (Azure/AWS/GCP), preferably Azure OpenAI

  . –

  Good to have

• Familiarity with MLOps/LLMOps concepts, CI/CD, monitoring, and observability.
• Strong problem-solving, communication, and stakeholder management skills.

Preferred Skills(Optional):

• Experience with open-source LLMs and model deployment (Hugging Face, vLLM, Ollama).
• Knowledge of NLP fundamentals and classical ML techniques.
• Exposure to AI governance, compliance, and enterprise security standards.
• Experience leading small teams or GenAI CoE initiatives.

Qualifications:

Btech in Computer Science, Engineering, Data Science, or related field from a reputed institution.