Mlops/AI Engineer - (GCP, Vertex AI, Watsonx, Agentic AI)

Key Responsibilities 

• End-to-End Solution Leadership: Own the full AI lifecycle: from requirement negotiation with stakeholders to technical architecture, deployment, and post-production monitoring. 
• GenAI & Agentic Orchestration: Design and scale sophisticated LLM applications using LangChain and LangGraph. Build multi-agent systems capable of reasoning, tool-use, and workflow automation/decision support for logistics use cases. 
• Hybrid Platform Oversight: Serve as the technical lead for our AI stack, optimizing Google Cloud Vertex AI for model training/serving and IBM Watsonx for enterprise-grade governance and scaling. 
• Production-Grade MLOps: Ensure the team builds robust CI/CD/CT (Continuous Training) pipelines. Oversee the integration of IaC, vector databases, feature stores, and automated evaluation frameworks. 
• Responsible AI & Governance: Implement model monitoring for drift, bias, and "hallucinations" (for GenAI) using watsonx.governance, ensuring compliance with enterprise standards. 
• People & Project Leadership: Translate business goals into measurable AI outcomes; manage backlog, risks, timelines, and documentation. Mentor a cross-functional team of AI Engineers and Data Scientists. Act as the "Player-Coach" who can perform deep code reviews while managing project milestones and executive expectations. 

Required Qualifications, Skills 

• 4+ years relevant in Data Science, MLE/MLOps, or GenAI Engineering; 1+ end-to-end project led to production (ownership of designdeploymonitor). 
• Depth in one track, plus working breadth across: 
• ML/DS: problem framing, feature engineering, model selection, evaluation. 
• GenAI: prompt engineering, RAG, agentic patterns, tool use. 
• MLOps: CI/CD for ML, observability, rollback, cost/perf tuning. 
• Stack: GCP/Vertex AI, IBM watsonx, Python, LangChain/LangGraph (or equivalent orchestration), Git, Docker/Kubernetes, SQL. 
• Proven ability to lead cross-functional contributors and deliver measurable business impact. 

Preferred Qualifications 

• Experience with Vertex AI (Pipelines, Workbench, Endpoints, Model/Feature Registry) and watsonx.ai / watsonx.governance. 
• Familiarity with vector databases and retrieval and RAG evaluation. 
• Exposure to MLflow/Kubeflow/Airflow, Terraform/Helm, and LLMs (Gemini, Llama, MistralAI). 
• Knowledge of responsible AI controls (evals, guardrails, red teaming) and basic compliance frameworks. 
• Experience with production monitoring/incident response for ML/GenAI services (SLAs, on-call, postmortems). 

Education 

• Bachelors degree in Computer Science, Information Systems, Engineering, or related field