Description

You will architect, fine-tune, and deploy Large Language Models (LLMs) and Generative AI techniques to improve business applications. You own the production deployment of high-performance, scalable ML pipelines and GPU-based inference systems in cloud environments.

Responsibilities

• Architect and implement RAG, PEFT, and SFT solutions for LLMs.
• Build and maintain scalable ML pipelines and inference systems on AWS, GCP, or Azure.
• Implement prompt engineering, adversarial testing, and model optimization strategies.
• Coordinate with offshore technical teams to define scope, timelines, and deliverables.
• Translate business requirements into technical specifications and communicate trade-offs to stakeholders.

Required Skills

• 10+ years of experience as an ML Engineer.
• 1-2 years dedicated to Generative AI or NLP projects.
• Strong proficiency in Python and deep learning frameworks (PyTorch or TensorFlow).
• Hands-on experience with LLMs (GPT-4, Llama), RAG architectures, LangChain, and Vector Databases.
• Experience with Docker, Kubernetes, and CI/CD tools for ML.
• Familiarity with AWS services including SageMaker, Bedrock, S3, Lambda, and Glue.
• Ability to work independently and troubleshoot issues in a fast-paced environment.

Preferred Skills

• Engineering degree in computer science or equivalent.
• Experience in the banking or financial services domain, specifically payments.

Key Skills

Education

Any Graduate