Senior Generative AI Engineer (Python, LLM, RAG)

Role Overview:

We are hiring experienced Generative AI Engineers to design and build enterprise-grade AI solutions within the banking domain. This role focuses on developing scalable LLM-powered applications, implementing advanced RAG pipelines, and deploying production-ready AI systems.

The ideal candidate will have strong Python expertise, hands-on experience with LLMs and GenAI frameworks, and a deep understanding of end-to-end AI/ML lifecycle and MLOps practices.

Key Responsibilities:

• Design and develop Generative AI applications using Large Language Models (LLMs)
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines with advanced techniques
• Develop scalable APIs and microservices using Python (FastAPI, Flask, etc.)
• Implement prompt engineering strategies, prompt tuning, and reusable templates
• Integrate LLM solutions with enterprise systems via APIs, knowledge graphs, and orchestration frameworks
• Work with vector databases (Pinecone, PGVector, Mongo Atlas, Neo4j) for semantic search and retrieval
• Handle and process large-scale unstructured datasets
• Deploy AI/ML models into production with strong MLOps practices
• Build and maintain CI/CD pipelines for AI solutions
• Ensure model performance, reliability, and safety using guardrails and evaluation frameworks
• Collaborate with cross-functional teams to deliver high-impact AI solutions

Required Skills & Qualifications:

Experience:

• 6–10 years in application development, AI/ML, or systems engineering

Core AI/ML Expertise:

• Strong foundation in:
  • Machine Learning & Data Science
  • Natural Language Processing (NLP)
  • Neural Networks & LLMs
  • Statistics

Generative AI & LLMs:

• Hands-on experience with:
  • OpenAI, Google Gemini, Anthropic Claude, Mistral, LLaMA
• Strong experience with:
  • RAG pipelines (must-have)
  • Prompt engineering & tuning
  • Agentic frameworks (LangChain, LlamaIndex, etc.)
  • Guardrails & GenAI evaluation techniques

Programming & Tools:

• Strong proficiency in Python
• Experience with:
  • Pandas, NumPy, scikit-learn
  • PyTorch / TensorFlow
  • Transformers, Hugging Face
  • FastAPI
  • LangChain, LlamaIndex

Data & Infrastructure:

• Experience with:
  • Vector databases: Pinecone, PGVector, MongoDB Atlas, Neo4j
  • Handling large-scale unstructured data

Deployment & MLOps:

• Experience deploying AI models to production
• Strong understanding of:
  • MLOps, model evaluation
  • CI/CD tools: Jenkins, GitLab CI, Azure DevOps, ArgoCD

Cloud & Containerization:

• Hands-on experience with:
  • Kubernetes / OpenShift
  • Cloud platforms (GCP, Azure, AWS preferred)

Soft Skills:

• Strong analytical and problem-solving skills
• Ability to work independently in complex environments
• Excellent communication and collaboration skills