Senior LLMOps Engineer

At Jacobs, we're challenging today to reinvent tomorrow by solving the world's most critical problems for thriving cities, resilient environments, mission-critical outcomes, operational advancement, scientific discovery and cutting-edge manufacturing, turning abstract ideas into realities that transform the world for good.

Your impact

Jacobs is looking for an experienced Senior LLMOps / AI Platform Engineer to join our team. In this role, you will help design, build, and operationalize enterprise-grade AI and GenAI solutions in cloud environments, with a strong focus on scalability, governance, observability, and production readiness. You will work across modern AI platforms and delivery teams to enable secure, reliable, and maintainable multi-agent, RAG, and conversational AI solutions that integrate with enterprise data landscapes and support real business outcomes.

Key Responsibilities

• Design, implement, and optimize MLOps and LLMOps practices for enterprise AI and GenAI solutions in cloud environments
• Build and manage CI/CD pipelines across development, UAT, and production environments to support controlled, reliable deployments
• Develop and maintain monitoring, observability, and operational dashboards covering system KPIs, usage analytics, model performance, and agent activity
• Implement versioning, traceability, and controlled prompt and configuration management for AI agents and GenAI components
• Support the productionization of multi-agent and RAG-based solutions that integrate structured and unstructured enterprise data sources
• Collaborate closely with AI engineers, data engineers, software engineers, architects, and product teams to ensure end-to-end delivery of scalable AI platforms
• Help design secure and governed AI solutions with strong access control, auditability, privacy controls, and hallucination mitigation mechanisms
• Contribute to testing and release processes including automated testing, smoke testing, integration testing, and performance validation
• Support backend and platform engineering activities using Python and cloud-native services
• Advise clients and internal stakeholders on platform design, operational governance, handover, and best practices for sustainable AI adoption
• Continuously improve engineering standards, automation, tooling, and delivery practices in line with evolving AI and business needs
  

Here's what you'll need

• 5+ years of experience in MLOps, DevOps, platform engineering, ML engineering, or related roles, with a strong focus on cloud-based solutions
• Strong proficiency in Python and experience supporting production grade software engineering practices
• Hands-on experience with Azure based environments and modern data and AI platforms such as Databricks and Fabric
• Experience building and managing CI/CD pipelines using Azure DevOps or equivalent tooling
• Strong understanding of model lifecycle management, deployment governance, monitoring, logging, and release processes for AI and ML systems
• Experience operationalizing GenAI, LLM, RAG, or multi-agent solutions in enterprise environments
• Knowledge of prompt management, model versioning, traceability, and observability practices for GenAI systems
• Experience designing or supporting secure enterprise solutions with role based access control, audit trails, and privacy safeguards
• Understanding of vector databases, metadata management, embeddings, indexing, and retrieval techniques to improve LLM output quality
• Experience working with structured and unstructured enterprise data to support scalable AI solutions
• Strong analytical and problem solving skills, with the ability to balance engineering quality, governance, and delivery speed
• Excellent communication and consulting skills, with the ability to work directly with stakeholders and contribute to architecture and operating model discussions