AI Engineering Manager – Agentic AI & Solution Delivery

Location: Remote

Department: Data Science & AI Innovation

Employment Type: Full-time

Recommended Experience: 7+ years of relevant experience in AI engineering, backend development, or software engineering, including 2+ years leading technical teams and project delivery.

Role Overview

We are looking for a hands-on AI Engineering Manager to lead the design, development, and delivery of enterprise AI solutions, with a robust focus on Google Agent Development Kit (ADK), agentic applications, and automated business workflows.

The role combines technical execution, people management, and delivery ownership. You will translate business requirements into solution designs and actionable project plans, organize work into achievable sprints, guide engineers through implementation, and drive solutions through testing, deployment, and production adoption.

You will remain actively involved in architecture, coding, code reviews, and technical troubleshooting while managing team capacity, sprint execution, dependencies, and stakeholder expectations.

Key Responsibilities

- Own End-to-End AI Solution Delivery

Translate business problems into clearly defined use cases, technical scope, and measurable acceptance criteria. Lead solution discovery, feasibility assessment, implementation, user acceptance testing, and production rollout. Make practical trade-offs between business value, technical complexity, delivery timelines, and operating cost.
- Provide Hands-On Engineering and System Design Leadership

Design modular services and APIs using Python and FastAPI, and contribute production code to critical components. Lead architecture discussions, review implementations, and resolve complex technical blockers. Guide decisions around service boundaries, data models, asynchronous processing, background jobs, caching, scalability, and failure recovery.

- Lead ADK-Based Agents and AI Workflow Development

Lead the development of single-agent and multi-agent applications using Google ADK, integrating enterprise data, custom tools, and business APIs. Design workflows that combine LLM-driven tasks with deterministic processing, structured outputs, state management, and human approval checkpoints. Guide RAG, document-processing, and tool-calling implementations, choosing the simplest architecture that meets the requirement.

- Manage Project Planning, Capacity, and Sprint Plans

Break initiatives into epics, user stories, and engineer-owned tasks with clear estimates, dependencies, and acceptance criteria. Maintain two-week sprint plans and multi-sprint delivery forecasts, accounting for team capacity, skill availability, leave, and production support. Coordinate AI, backend, frontend,



and platform work so that each sprint advances a usable business capability.

- Drive Scrum Execution and Delivery Discipline

Lead or facilitate backlog refinement, sprint planning, daily stand-ups, sprint reviews, and retrospectives in collaboration with product and delivery stakeholders. Maintain clear sprint goals, remove blockers, and manage scope changes without overcommitting the team. Use Jira, Notion, or equivalent tools to keep ownership, progress, dependencies, and delivery risks visible, and turn retrospective findings into concrete improvements.

- Lead, Coach, and Develop the Engineering Team

Manage and mentor AI engineers, providing regular feedback, technical coaching, and development support. Delegate meaningful ownership while remaining available for hands-on problem-solving. Support hiring, onboarding, performance reviews, and knowledge sharing, and establish consistent expectations for code quality, documentation, collaboration, and accountability.
- Own Engineering Quality, DevOps, and Production Readiness

Establish practical standards for automated testing, AI evaluation, code reviews, and release readiness. Guide Docker-based deployments, CI/CD pipelines, environment configuration, secrets management, monitoring, and rollback procedures with platform teams. Ensure solutions include appropriate access controls, validation, auditability, and human review, and investigate production issues across application code, model behavior, integrations, and infrastructure.

- Align Stakeholders and Demonstrate Outcomes

Work closely with business owners, product managers, security, and infrastructure teams to align priorities and resolve dependencies. Communicate delivery forecasts, risks, trade-offs, and decisions clearly to technical and non-technical stakeholders. Track both engineering performance and business outcomes, including workflow quality, reliability, adoption, turnaround time, and cost.

Required Qualifications and Skills

Experience and Leadership

- 7+ years of relevant engineering experience, including hands-on delivery of production AI or LLM applications, with 2+ years leading technical teams and delivery. The experience requirement applies to overall engineering experience, not years using ADK.
- Demonstrated ability to manage multiple workstreams, build realistic project and sprint plans,



allocate resources, and take solutions from requirements through production.
- Experience mentoring engineers, conducting code and design reviews, managing delivery risks, and communicating with business stakeholders.

Technical and Delivery Capabilities

- Hands-on software engineering

Strong Python, FastAPI, asynchronous programming, API integration, structured validation, testing, and production debugging.
- ADK and agentic workflows

Practical experience with Google ADK, custom tools, agent orchestration, session and state management, context handling, and human-in-the-loop workflows.
- LLM application development

Experience with RAG, embeddings, vector search, prompt and context engineering, function calling, structured outputs, and AI evaluation.
- System design

Ability to design maintainable AI services and explain trade-offs involving reliability, scalability, security, latency, and cost.
- Cloud and DevOps

Working proficiency with Docker, Git, CI/CD, Linux fundamentals, secrets management, monitoring, and cloud deployment, preferably on GCP.
- Project and Scrum management

Backlog decomposition, estimation, capacity planning, sprint goals, acceptance criteria, dependency tracking, and delivery forecasting using Jira, Notion, or similar tools.
- People and stakeholder management

Coaching, delegation, performance feedback, cross-functional coordination, and clear communication of technical decisions and delivery risks.

Preferred Skills

- Familiarity with Vertex AI, Cloud Run, Cloud Storage, BigQuery, and MongoDB Atlas or comparable cloud and data services.
- Exposure to ADK, LangGraph, MCP-based tool integrations, A2A interoperability, and enterprise document-processing workflows.
- Experience with infrastructure as code, AI observability, security reviews, or regulated enterprise environments. Agile or Scrum certification is beneficial but not a substitute for demonstrated delivery experience.

What Success Looks Like

- Predictable delivery: The team works against clear sprint goals and realistic forecasts, with dependencies and risks addressed early.
- Strong engineering: Solutions are maintainable, evaluated, secure, observable, and ready for production—not limited to demonstrations.
- Effective team ownership: Engineers understand their priorities, receive timely technical guidance, and grow in capability and independence.
- Measurable business value: Delivered AI workflows are adopted and demonstrate improvements in quality, turnaround time, or operational efficiency.

The ideal candidate can design the solution, contribute to the code, plan the sprint, develop the team, and remain accountable for the outcome.

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