ML engineer with Gen AI

Job Description:

• GenAI Development: Architect, fine-tune, and deploy Large Language Models (LLMs) and Generative AI techniques (e.g., RAG, PEFT/SFT) to improve business applications.
• Production Deployment: Build and maintain high-performance, scalable ML pipelines and GPU-based inference systems in cloud environments (AWS/GCP/Azure).
• Collaboration & Communication: Work closely with product managers, data scientists, and engineers to translate business requirements into technical specifications.
• Stakeholder Engagement: Clearly present AI methodologies, performance results, and technical trade-offs to non-technical stakeholders and leadership.
• Model Optimization: Implement prompt engineering, adversarial testing, and model optimization strategies to ensure high-quality, efficient, and safe outputs.
• Stay Updated: Actively keep up with the latest advancements in GenAI research and incorporate them into our production systems.
• Team and project Coordination: Define project scope, timelines, deliverables, success metrics, co-ordinate with and guide offshore technical team on project deliverables.
  

Required Skills & Qualifications

• Experience: 10+ years of experience as an ML Engineer, with at least 1-2 years dedicated to Generative AI or NLP projects, and good experience on AWS cloud platform.
• Technical Expertise: Strong proficiency in Python, and IDEs such as Cursor/AWS Kiro, deep learning frameworks (PyTorch or TensorFlow or), utilizing GitHub co-pilot etc.
• GenAI Proficiency: Hands-on experience with LLMs (e.g., GPT-4, Llama), RAG architectures, LangChain, Vector Databases, Knowledge graphs, and Agentic AI
• MLOps and LLM Ops: Familiarity with Docker, Kubernetes, and CI/CD tools for ML.
• AWS Skills: S3, Lambda, Glue, AWS Sage maker, and AWS Bedrock platform
• Communication Skills: Excellent verbal and written communication skills; ability to articulate complex technical concepts simply.
• Stakeholder Management: Able to collaborate with key business/client stakeholders and manage their expectations
• Problem-Solving: Proven ability to work independently in a fast-paced environment and troubleshoot issues.
  

Preferred Qualifications

• Engineering degree in computer science or equivalent, and relevant certification in Machine learning 
• Experience in banking or financial services domain – Payments industry.