Sr. AI LLMOps Engineer / Lead

TalentOla; Eden Prairie (MN); On-site; USD 140,000 - 190,000; Full time

Job summary

TalentOla in Minnesota seeks a Sr. AI LLMOps Engineer / Lead to drive delivery, operationalization, governance, and scaling of enterprise-grade AI solutions and intelligent automation platforms.

You will lead AI engineering initiatives, managing sprint delivery, SDLC compliance, and integration across platforms while collaborating with Governance, Architecture, and QA teams to ensure reliability, security, and scalability of AI systems in production.

Qualifications

• 3+ years of experience leading engineering teams, delivery organizations, or large-scale technology initiatives
• Hands-on experience designing, delivering, and operationalizing production AI solutions leveraging large language models (LLMs), APIs, agentic workflows, orchestration frameworks, and modern AI engineering patterns
• Experience implementing and scaling engineering operating models, AI delivery frameworks, agile delivery ecosystems, or enterprise engineering practices
• Strong analytical, problem-solving, communication, presentation, stakeholder management, and cross-functional collaboration skills
• Ability to manage multiple priorities in fast-paced, evolving environments
• Experience with cloud platforms, APIs, data integration, DevOps practices, automation frameworks, and modern software engineering tools preferred
• Experience operating in healthcare or other regulated environments preferred Strong understanding of responsible AI concepts, including governance.

Responsibilities

• Lead the execution and delivery of enterprise AI engineering initiatives, including AI-powered applications, LLM-enabled workflows, agentic orchestration solutions, AI-enabled automation capabilities, and platform integrations
• Drive day-to-day engineering delivery activities across AI teams, including sprint execution, backlog management, delivery tracking, issue resolution, dependency management, and operational execution
• Implement and operationalize enterprise AI engineering practices, including AI software development lifecycle (SDLC) processes, deployment standards, runtime observability, release management, and engineering quality practices
• Provide technical oversight across solution design, development, validation, deployment, monitoring, optimization, and production support activities
• Support AIOps and LLMOps operational practices, including runtime monitoring, drift detection, observability, incident management, prompt lifecycle management, evaluation execution, operational telemetry, and production reliability
• Develop reusable AI engineering patterns, implementation playbooks, shared services, templates, internal libraries, and engineering accelerators to improve delivery consistency, scalability, and operational efficiency
• Drive adoption of enterprise engineering standards, scalable delivery practices, and shared implementation patterns across AI delivery teams
• Partner with AI Governance, Quality Engineering, Automation, Architecture, and AI Delivery Lifecycle teams to operationalize governance requirements, validation processes, responsible AI controls, runtime safeguards, and secure delivery practices
• Coordinate AI delivery activities across teams, including operational planning, resource management, contractor and vendor alignment, knowledge transfer, and delivery continuity
• Partner with cross-functional stakeholders to support technical feasibility assessments, delivery readiness activities, implementation planning, and engineering sustainability efforts
• Support vendor evaluations, platform implementation initiatives, build-versus-buy assessments, and engineering modernization efforts
• Lead, mentor, and develop engineering managers, architects, engineers, and contractor teams while fostering a high-performing, collaborative, and continuously learning culture
• Communicate delivery progress, operational risks, technical updates, engineering tradeoffs, and implementation recommendations to technical and business leaders
• Research and evaluate emerging AI engineering, automation, observability, orchestration, and platform technologies to support innovation and continuous improvement

Skills

Software engineering

AI/LLMOps

APIs

Agentic AI

Orchestration frameworks

Delivery leadership

Cloud platforms

DevOps practices

Cross-functional collaboration

Problem solving

Stakeholder management

Education

Engineering Degree BE/ME/BTech/MTech/BSc/MSc

Technical certifications in multiple technologies desirable

Job description

What is in it for you?

Seeking a Sr. AI LLMOps Engineer / Lead with expertise in AIOps, LLMOps, Agentic AI, APIs, and orchestration frameworks, driving the delivery, operationalization, governance, and scaling of enterprise-grade AI solutions and intelligent automation platforms

Roles and Responsibilities:

Lead the execution and delivery of enterprise AI engineering initiatives, including AI-powered applications, LLM-enabled workflows, agentic orchestration solutions, AI-enabled automation capabilities, and platform integrations

Drive day-to-day engineering delivery activities across AI teams, including sprint execution, backlog management, delivery tracking, issue resolution, dependency management, and operational execution

Implement and operationalize enterprise AI engineering practices, including AI software development lifecycle (SDLC) processes, deployment standards, runtime observability, release management, and engineering quality practices

Provide technical oversight across solution design, development, validation, deployment, monitoring, optimization, and production support activities Support AIOps and LLMOps operational practices, including runtime monitoring, drift detection, observability, incident management, prompt lifecycle management, evaluation execution, operational telemetry, and production reliability

Develop reusable AI engineering patterns, implementation playbooks, shared services, templates, internal libraries, and engineering accelerators to improve delivery consistency, scalability, and operational efficiency

Drive adoption of enterprise engineering standards, scalable delivery practices, and shared implementation patterns across AI delivery teams

Partner with AI Governance, Quality Engineering, Automation, Architecture, and AI Delivery Lifecycle teams to operationalize governance requirements, validation processes, responsible AI controls, runtime safeguards, and secure delivery practices

Coordinate AI delivery activities across teams, including operational planning, resource management, contractor and vendor alignment, knowledge transfer, and delivery continuity

Partner with cross-functional stakeholders to support technical feasibility assessments, delivery readiness activities, implementation planning, and engineering sustainability efforts

Support vendor evaluations, platform implementation initiatives, build-versus-buy assessments, and engineering modernization efforts

Lead, mentor, and develop engineering managers, architects, engineers, and contractor teams while fostering a high-performing, collaborative, and continuously learning culture

Communicate delivery progress, operational risks, technical updates, engineering tradeoffs, and implementation recommendations to technical and business leaders

Research and evaluate emerging AI engineering, automation, observability, orchestration, and platform technologies to support innovation and continuous improvement

Experie

Educational Qualifications: -

• Engineering Degree BE/ME/BTech/MTech/BSc/MSc.
• Technical certification in multiple technologies is desirable.

Skills: -

Mandatory skills

• Experience in software engineering, AI application engineering, engineering delivery, platform engineering, or enterprise technology functions required
• 3 or more years of experience leading engineering teams, delivery organizations, or large-scale technology initiatives required
• Experience leading distributed teams, contractor/vendor coordination, and large-scale engineering delivery initiatives within complex and evolving operational environments required
• Hands‑on experience designing, delivering, and operationalizing production AI solutions leveraging large language models (LLMs), APIs, agentic workflows, orchestration frameworks, and modern AI engineering patterns required
• Experience implementing and scaling engineering operating models, AI delivery frameworks, agile delivery ecosystems, or enterprise engineering practices required
• Strong analytical, problem-solving, communication, presentation, stakeholder management, and cross-functional collaboration skills required
• Ability to manage multiple priorities in fast-paced, evolving, and deadline‑driven environments required
• Experience with cloud platforms, APIs, data integration, DevOps practices, automation frameworks, and modern software engineering tools preferred
• Experience operating in healthcare or other regulated environments preferred Strong understanding of responsible AI concepts, including governance.