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.