Sr. Python Engineer, Agentic AI

Make your mark at Comcast -- a Fortune 30 global media and technology company. From the connectivity and platforms we provide, to the content and experiences we create, we reach hundreds of millions of customers, viewers, and guests worldwide. Become part of our award-winning technology team that turns big ideas into cutting-edge products, platforms, and solutions that our customers love. We create space to innovate, and we recognize, reward, and invest in your ideas, while ensuring you can proudly bring your authentic self to the workplace. Join us. You’ll do the best work of your career right here at Comcast. (In most cases, Comcast prefers to have employees on-site collaborating unless the team has been designated as virtual due to the nature of their work. If a position is listed with both office locations and virtual offerings, Comcast may be willing to consider candidates who live greater than 100 miles from the office for the remote option.)

Job Summary

We are looking for a Senior Software Engineer to lead the technical direction of our AI Agent initiatives. This is a backend engineering role first, focused on building scalable, reliable systems that happen to use modern AI techniques.

You will architect and operate production‑grade backend services and agentic workflows that solve complex, non‑deterministic problems. While LLMs are a core capability, success in this role comes from strong systems thinking, designing for correctness, observability, failure recovery, and long‑term maintainability.

You will serve as a technical anchor for the team—mentoring junior engineers, setting engineering standards, and working with leadership to turn AI concepts into dependable, customer‑facing systems.
-level machine learning expertise and innovation capacity are essential.

Job Description

What You Will Do

Architectural Leadership

Design and own scalable backend architectures and complex agent‑driven workflows. This includes service boundaries, data flows, failure handling, observability, and performance characteristics—not just model interaction.

Agent Behavior Engineering (Not Just Prompting)

Design and productionize agent behaviors including tool invocation, state management, memory strategies, and reasoning control. Prompts are treated as code: versioned, tested, reviewed, and evolved alongside backend logic for reliability, cost, and latency.

Systemic Quality & Code Health

Set the bar for clean, readable, maintainable code across the Python backend. Establish patterns and standards that allow AI‑heavy systems to remain understandable and operable over time.

Testing & Evaluation of Probabilistic Systems

Design testing strategies for non‑deterministic AI components, including behavioral regression testing, automated evaluations, and guardrails that reduce blast radius in production.

Technical Mentorship

Lead code reviews as a teaching tool, run architectural discussions, and actively mentor Engineer 1–2 developers in backend fundamentals, system design, and operational thinking.

Operational Excellence & Production Ownership

Own CI/CD, deployment, and runtime health. Lead production support efforts and help build a strong on‑call culture where systems are observable, diagnosable, and resilient under real‑world load.

Customer‑Facing Engineering

Partner with product and design to deliver high‑traffic, customer‑facing experiences where AI responses must meet clear standards for latency, reliability, and UX—not just correctness.

Cross‑Functional Technical Leadership

Translate product goals into pragmatic technical roadmaps, identifying scaling risks, technical debt, and AI‑specific failure modes early.

Advanced Debugging & Incident Leadership

Lead investigations into complex issues spanning multiple services, data pipelines, or emergent agent behaviors in production.

What We’re Looking For

Senior Backend Engineering Depth

Extensive experience designing, building, and operating backend systems in Python, including asynchronous frameworks (FastAPI, asyncio) and distributed service architectures.

Production Experience at Scale

A track record of owning customer‑facing systems in production. You understand uptime, latency budgets, incident response, and the cost of unreliable behavior.

AI Systems Intuition

Hands‑on experience integrating LLMs into real systems, with a clear understanding of their limitations, failure modes, and operational risks. You think in terms of workflows, control flow, and safeguards—not just prompt quality.

Agent & Tooling Experience

Experience building AI agents that interact with tools, APIs, and external systems,