AI Agent ML Engineer

Key Responsibilities

• Architect and develop multi-agent systems for process automation and intelligent decision-making.
• Implement reasoning, planning, and tool-use capabilities using frameworks such as LangChain, AutoGPT, or similar.
• Integrate AI agents with APIs, enterprise systems, and business workflows to drive operational efficiency.
• Analyze, map, and document business processes; translate process requirements into agentic frameworks and automation solutions.
• Collaborate with business stakeholders to identify opportunities for agent-driven process improvement.
• Develop, fine-tune, and deploy large language models (LLMs) and domain-specific ML models.
• Build and maintain data pipelines, embeddings, and vector databases to support agent intelligence.
• Optimize models for scalability, latency, and accuracy in production environments.
• Champion Responsible AI practices, including monitoring, guardrails, and compliance with governance standards.
• Lead Agile AI Operations, including backlog management, sprint planning, cross-functional collaboration, and continuous improvement.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Engineering, or related field.
• 3+ years’ experience in AI/ML engineering, agent development, process engineering, or related roles.
• Proven track record of building and deploying production-grade AI agents and ML models.
• Deep expertise in at least three of the following: Agent orchestration, ML model development, Data engineering, API integration, Process engineering, Responsible AI.
• Strong programming skills in Python; experience with ML frameworks (PyTorch, TensorFlow) and agent orchestration tools.
• Experience in business process analysis, process mapping, and workflow automation.
• Familiarity with prompt engineering and vector databases (e.g., Pinecone, Weaviate).
• Success operating in a matrix environment, developing strong relationships across functional groups (e.g., IT, business, UX) to achieve shared goals.
• Experience managing AI backlogs, sprints, and team metrics within Agile methodologies (e.g., Scrum, SAFe).
• Excellent leadership, communication, and presentation skills, with the ability to engage and influence internal and external stakeholders, and proven sound business judgment and financial acumen.