Description

You will architect, fine-tune, and deploy Large Language Models and Generative AI techniques to enhance business applications. You own the full lifecycle from RAG implementation to production deployment on AWS.

Responsibilities

• Architect and deploy LLM solutions using RAG, PEFT, and SFT techniques.
• Build scalable ML pipelines and GPU-based inference systems in cloud environments.
• Implement prompt engineering, adversarial testing, and model optimization strategies.
• Define project scope and guide offshore technical teams on deliverables.
• Translate business requirements into technical specifications and present trade-offs to stakeholders.

Required Skills

• 10+ years of experience as an ML Engineer.
• 1-2 years dedicated to Generative AI, NLP, or LLM projects.
• Strong proficiency in Python and deep learning frameworks (PyTorch or TensorFlow).
• Hands-on experience with LLMs (GPT-4, Llama), LangChain, and Vector Databases.
• Experience with AWS SageMaker, Bedrock, S3, Lambda, and Glue.
• Familiarity with Docker, Kubernetes, and CI/CD tools for ML.
• Experience using AI-assisted coding tools like GitHub Copilot or Cursor.
• Ability to articulate complex technical concepts to non-technical stakeholders.

Preferred Skills

• Engineering degree in Computer Science or equivalent.
• Experience in the banking or financial services domain.

Key Skills

Education

Any Graduate