Description

Architect, fine-tune, and deploy Large Language Models (LLMs) and Generative AI techniques to improve business applications. Build and maintain high-performance, scalable ML pipelines and GPU-based inference systems in cloud environments.

This role is on-site.

Responsibilities

• Architect and deploy LLMs using RAG, PEFT, and SFT techniques.
• Build scalable ML pipelines and GPU-based inference systems on AWS, GCP, or Azure.
• Implement prompt engineering, adversarial testing, and model optimization for safe, efficient outputs.
• Define project scope, timelines, and deliverables while guiding offshore technical teams.
• 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 or NLP projects.
• Strong proficiency in Python and deep learning frameworks (PyTorch or TensorFlow).
• Hands-on experience with LLMs (GPT-4, Llama), LangChain, Vector Databases, and Agentic AI.
• Expertise in AWS cloud platform including SageMaker, Bedrock, S3, Lambda, and Glue.
• Familiarity with Docker, Kubernetes, and CI/CD tools for MLOps and LLM Ops.
• Experience with IDEs such as Cursor, AWS Kiro, or GitHub Copilot.

Preferred Skills

• Engineering degree in Computer Science or equivalent with relevant Machine Learning certification.
• Experience in the banking or financial services domain, specifically payments.

Key Skills

Education

Any Graduate