Machine Learning Engineer

Overview

Senior technical authority for designing and delivering next-generation agentic AI and LLM-powered solutions, including production infrastructure.

What you'll do

• Architect and deliver agentic AI systems using LLMs and multi-agent workflows.
• Prototype and translate ambiguous business problems into deployable AI architectures.
• Provide technical oversight to keep delivery teams aligned to design intent.
• Evaluate agent behavior to help ensure production system robustness.
• Develop and optimize MLOps pipelines, including RAG, vector databases, and retrieval strategies.
• Build prompt frameworks, evaluation pipelines, and guardrails for safe AI behavior.
• Integrate LLM systems with enterprise APIs, data platforms, and operational systems, plus observability and monitoring.

What you'll need

• 6–8 years of professional software engineering experience with agentic AI and/or ML platform engineering.
• 5+ years of hands-on experience with generative AI and LLM-based systems in production environments.
• Experience building multi-agent systems or autonomous workflows.
• Ability to structurally debug AI production issues and apply fixes.
• Solid grounding in core data science and machine learning.
• Practical experience with LLMs/generative AI, including prompting and RAG.
• Strong Python skills and common data/ML libraries, with good engineering habits.
• Educational qualification in relevant fields such as Computer Science/IT/Electronics/Data Science/ML/AI Engineering.
• Clear communication and collaboration skills.

Nice to have

• Exposure to cloud AI platforms (Azure preferred).
• Experience practicing MLOps and LLMOps.
• Experience mentoring junior colleagues.
• Interest in consulting and client-facing delivery.

Details

• Works across multiple client engagements simultaneously.