Manager of AI Integration & Agentic Analytics Engineering

Required Skills & Experience

8+ years of progressive software engineering, AI/ML engineering, data engineering, or related technical experience. 2+ years of direct people management or formal engineering leadership experience, including responsibility for engineers' delivery, development, prioritization, and performance. Strong technical understanding of Generative AI, LLMs, and agentic AI architectures. Hands-on experience developing or significantly contributing to GenAI or agentic AI solutions. Experience leading technical teams delivering complex software or AI initiatives in an enterprise environment. Strong understanding of contemporary AI engineering concepts including APIs, RAG, vector retrieval, agent/tool integration, evaluation, monitoring, and cloud-based architectures. Ability to participate credibly in architecture and engineering discussions while effectively delegating implementation to senior engineers. Demonstrated experience working across engineering, product, architecture, security/governance, and business stakeholders. Excellent written and verbal communication skills with demonstrated ability to communicate with senior and executive stakeholders.

Nice to Have Skills & Experience

Experience leading teams that have moved Generative AI or agentic AI solutions into production. Experience with agentic frameworks or platforms such as LangGraph/LangChain, Semantic Kernel, AutoGen, CrewAI, or comparable technologies. Experience with enterprise AI ecosystems such as Azure OpenAI, AWS Bedrock, Google Vertex AI, or equivalent platforms. Experience establishing governance, evaluation, monitoring, or operational standards for enterprise AI solutions. Experience managing senior/principal-level engineers working on emerging technologies. Experience with Responsible AI, AI security, data privacy, model risk, observability, or human-in-the-loop controls. Experience creating reusable AI platforms, frameworks, or patterns used across multiple business use cases.

Job Description

We are seeking a hands-on Agentic AI Engineering Manager to lead a team building and scaling enterprise AI and agentic solutions.

This role combines technical leadership with people management and cross-functional execution. The ideal candidate has enough hands-on experience with agentic AI, Generative AI, LLMs, and modern AI engineering to provide technical direction and actively contribute when needed, but this is not primarily an individual contributor role.

You will manage a team that includes senior technical AI engineering resources responsible for the deepest hands-on development. Your responsibility is to ensure that the team is building the right solutions, maintaining strong technical and governance standards, and successfully moving AI initiatives from experimentation into reliable enterprise capabilities.

Unlike a pure AI architect or senior engineer position, success in this role requires strong people leadership, executive presence, stakeholder management, technical judgment, and delivery ownership.

Key Responsibilities

People & Engineering Leadership Lead and manage a team of AI/ML and software engineering resources focused on Generative AI and agentic AI solutions. Provide coaching, technical guidance, prioritization, performance management, and career development for engineering team members. Create an environment where senior engineers can operate with significant technical autonomy while remaining aligned to broader architecture, governance, and business objectives. Translate business priorities into clear engineering direction, milestones, and ownership. Balance hands-on technical involvement with the ability to delegate deep implementation work to senior technical resources. Agentic AI & Technical Leadership Provide technical leadership for solutions leveraging LLMs, AI agents, multi-agent architectures, tool/function calling, RAG, APIs, orchestration frameworks, and enterprise data sources. Participate in architecture and design discussions and provide hands-on technical contributions when appropriate. Guide engineering decisions around areas such as agent orchestration, model selection, prompt design, context management, evaluation, observability, security, scalability, and integration patterns. Partner closely with senior AI engineers who own significant portions of the hands-on design and development. Evaluate emerging AI technologies and determine where they can create practical enterprise value. Enterprise Delivery & Governance Drive AI initiatives through the engineering lifecycle from discovery and proof of concept through productionization and ongoing operational support. Establish appropriate engineering and governance practices around AI safety, security, model/agent evaluation, observability, access controls, data privacy, human oversight, and responsible AI. Ensure solutions meet enterprise expectations for reliability, maintainability, scalability, and security. Partner with architecture, security, data, platform, risk/governance, and business teams to identify and resolve barriers to production adoption. Establish repeatable standards that allow agentic AI capabilities to scale beyond individual pilots or use cases. Stakeholder & Executive Leadership Serve as a primary technical and delivery leader between AI engineering teams and business stakeholders. Communicate complex AI concepts, technical tradeoffs, risks, dependencies, and recommendations clearly to both technical and non-technical audiences. Provide concise updates and recommendations to senior and executive leadership. Manage competing priorities and expectations across engineering, product, architecture, governance, and business teams. Help stakeholders distinguish between promising AI experimentation and solutions that are realistically ready for enterprise adoption.

What Success Looks Like

This person does not need to be the strongest individual AI developer on the team.

They should be able to:

Manage and develop highly technical AI engineers. Understand the architecture deeply enough to challenge assumptions and make sound technical decisions. Jump into the technology when necessary without becoming the team's primary day-to-day coder. Give senior engineers room to own deep technical implementation. Build alignment across technical and business organizations. Communicate effectively with executives. Put appropriate enterprise governance and engineering discipline around emerging AI capabilities. Turn AI experimentation into an organized, scalable engineering program.