Description

Architect and deploy Large Language Models and Generative AI solutions to enhance business applications. You will own the end-to-end lifecycle from model fine-tuning to scalable production deployment.

Responsibilities

• Design, fine-tune, and deploy LLMs using RAG, PEFT, and SFT techniques to improve application performance.
• Build and maintain high-performance, scalable ML pipelines and GPU-based inference systems in cloud environments.
• Implement prompt engineering, adversarial testing, and optimization strategies to ensure safe and efficient model outputs.
• Define project scope, timelines, and deliverables while guiding offshore technical teams.
• Translate business requirements into technical specifications and communicate trade-offs to stakeholders.

Required Skills

• 10+ years of experience as an ML Engineer with 1-2 years dedicated to Generative AI or NLP projects.
• Strong proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Hands-on experience with LLMs (e.g., GPT-4, Llama), RAG architectures, LangChain, Vector Databases, and Knowledge graphs.
• Experience with Agentic AI and model optimization strategies.
• Proficiency in cloud platforms (AWS/GCP/Azure), specifically AWS SageMaker, Bedrock, S3, Lambda, and Glue.
• Familiarity with Docker, Kubernetes, and CI/CD tools for MLOps/LLMOps.
• Ability to articulate complex technical concepts clearly to non-technical stakeholders.

Preferred Skills

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

Key Skills

Education

Any Graduate