Agentic​/ML Ops Engineer

Job Description

We are seeking an Agentic AI Engineer to join a clients AI Center of Excellence. This hands‑on role involves designing, developing, testing, and deploying AI agents that can perceive their environment, make decisions, and take autonomous actions. You’ll collaborate with AI Architects, Data Scientists, and business stakeholders to build innovative solutions for areas like supply‑chain optimization, intelligent automation, and enhanced customer/vendor experiences.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances.

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• Bachelor’s or Master’s in Computer Science, AI, ML, or related field
• 3–5+ years in software engineering with AI/ML focus

• Strong Python skills and AI/ML libraries (e.g., scikit-learn, Pandas, Num Py, Hugging Face)
• Experience with agentic AI frameworks (e.g., Google ADK, Lang Chain, Auto Gen, CrewAI)
• Working with LLMs for NLU, reasoning, and function calling (e.g., GPT, Claude, Gemini)
• API development/integration (REST, gRPC)
• Cloud platforms (GCP, AWS, Azure) and AI services (Vertex AI)
• CI/CD, Git, software engineering best practices

• Deployment and operationalization of models/agents
• Problem‑solving, debugging, and collaboration in agile environments
• Strong communication skills
• Experience deploying AI solutions in production
• Familiarity with MLOps/LLMOps practices
• Containerization (Docker, Kubernetes)
• Knowledge of vector databases and RAG systems
• Basic data engineering concepts
• Automated testing frameworks
• Open‑source contributions
• Domain knowledge in supply chain, logistics, or enterprise automation