Senior GenAI Engineering Lead — Planning & Execution (Python)
Confirmed on the employer's own hiring board on Nov 5, 2026. First seen by Alion on Nov 4, 2026. Evolver scores A on the Alion truth index.
About Us
We are an innovative AI company with a team of 100+ people focused on transforming core enterprise functions in Fortune 500 companies. Our platform combines a rich understanding of a company’s current state with deep domain expertise to execute these functions with accuracy and consistency through code, statistical methods, and guided ML and LLM execution. It handles processes end-to-end, engaging humans when needed to maximize efficiency and quality.
Join us at the forefront of AI innovation, where your expertise will directly shape the future of enterprise operations.
Role Overview
We are looking for a hands-on engineering lead to lead a small team of developers and own the roadmap, architecture, and delivery of the planning and execution layer of our AI platform.
This layer translates user goals into actionable plans and coordinates agents, tools, data, and human input to carry those plans through to completion. You will define how workflows are planned, executed, monitored, and adapted as new information becomes available.
Working closely with product leaders, domain experts, and engineering teams, you will turn business needs into a prioritized platform roadmap and guide your team in delivering it. This role combines team leadership, including setting direction, coordinating work, and coaching developers, with substantial hands-on Python development from early design through production operation.
Key Responsibilities
- Own the roadmap and delivery: Define priorities for the platform’s planning and execution layer, translate them into milestones, and lead delivery aligned with customer needs and business goals.
- Evolve the platform architecture: Lead the evolution of the planner and executor architecture, defining component boundaries, interfaces, and state management to support scalability, extensibility, and reliability.
- Advance planning and orchestration: Develop capabilities for goal decomposition, agent and tool selection, dependency management, and adaptive replanning based on execution feedback.
- Deliver reliable, observable execution: Build resilient workflows with persistent state, failure recovery, and human oversight. Establish evaluations and monitoring to improve quality, latency, and cost.
- Lead a small development team: Set technical direction, coordinate work, coach developers, and maintain engineering quality while writing and reviewing production Python code. Collaborate with product, domain, infrastructure, and security teams to deliver enterprise-ready capabilities.
Qualifications:
Experience:
- 8+ years of professional software engineering experience, with advanced proficiency in Python.
- Experience owning a technical roadmap and/or leading a small development team to deliver software.
- Hands-on experience building production-level AI-powered workflows or agent systems
Education:
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
Technical Skills:
- Strong Python engineering skills, including API design, asynchronous programming, testing, and production debugging.
- Experience with LLM-based planning, tool calling, structured outputs, context management, and agent orchestration.
- Ability to design execution systems that handle partial failures, retries, concurrency, and persistent state.
- Experience evaluating AI behavior and using traces, metrics, and logs to improve system performance.
- Familiarity with retrieval-augmented generation and integrating structured and unstructured data into agent workflows.
- Experience with cloud-native services, databases, queues, and deployment practices.
Soft Skills:
- Ability to turn ambiguous requirements into a clear roadmap and achievable delivery milestones.
- Strong architectural judgment and the ability to communicate technical tradeoffs.
- Experience collaborating with product managers, domain experts, and engineering teams.
- A practical approach to balancing experimentation with production reliability.
- Comfort taking ownership in a fast-moving startup environment.
Benefits
- Competitive Compensation: Tailored to your experience and skill set.
- Flexible Work Arrangements: Hybrid working model.
- Career Growth: Opportunities to expand your technical and leadership responsibilities.
- Meaningful Impact: Direct ownership of core platform capabilities that shape how AI delivers professional services.