About the role
Channel Fusion is building an AI Factory focused on accelerating software delivery, automating business operations, modernizing legacy platforms, and creating intelligent customer-facing solutions through Agentic AI.
This is a hands-on implementation role. Rather than owning a single client program, this person builds and ships AI agents and automation across Channel Fusion’s teams and client solutions — wherever the Lead AI Platform Engineer and Field Product Managers identify agent opportunities. You’ll work day-to-day alongside Suraj (AI Engineer) and under the direction of the Lead/Senior AI Platform Engineer, picking up implementation work as it’s scoped and prioritized across programs.
This role does not set agent architecture standards or product requirements — those come from the Lead AI Platform Engineer (how) and the relevant FPM (what). This role is the execution capacity that turns those specs into working, production-quality agents.
What You Own
- Implementation. Build, test, and ship individual agents and automations to the architecture and standards set by the Lead AI Platform Engineer.
- Cross-Team Delivery. Pick up agent-building work across whichever Channel Fusion team or client solution has current priority, rather than owning one fixed program.
- Peer Collaboration. Work directly with Suraj and other AI engineers to share patterns, reusable components, and avoid duplicated effort across parallel implementations.
- Quality & Testing. Write and maintain evals/tests for the agents you build so regressions are caught before they reach production.
- Feedback Loop. Flag gaps, edge cases, or ambiguity in a spec back to the Lead/FPM rather than guessing silently.
What You Do NOT Own
- Agent architecture, orchestration standards, and platform-wide design decisions. This is owned by the Lead AI Platform Engineer.
- What a given agent needs to accomplish. This is owned by the relevant Field Product Manager.
- Requirements for agents touching a specific engineering workstream. Owned by the relevant Lead Developer for engineering-embedded agents (e.g., CI/CD or code-review agents).
- The ADLC framework and AI governance policy. This is owned by the Technology Director and this role builds within it.
Key Responsibilities
Agent Implementation
- Build agents against specs handed down from the Lead AI Platform Engineer and FPMs — covering planning, tool use, and task execution.
- Implement retrieval-augmented generation (RAG) components against enterprise knowledge sources.
- Wire agents into internal and external business systems via secure APIs and data access patterns already established by the platform team.
- Reuse and extend existing agent frameworks/libraries rather than building one-offs.
Cross-Program Delivery
- Move across Channel Fusion client programs and internal workflows as implementation priorities shift.
- Ramp quickly on a new program’s data model and requirements with support from the assigned FPM.
- Keep implementation consistent with patterns used elsewhere in the AI Factory, flagging when a program’s needs don’t fit existing patterns.
Quality, Testing & Operations
- Write evaluation/test cases for agents before they ship.
- Monitor and troubleshoot agents in production; escalat arc-level issues to the Lead.
- Track and report token/cost usage for agents you own.
Qualifications
Require
- 2+ years of professional software engineering experience.
- Hands-on experience building or shipping at least one production AI-enabled application or agent.
- Strong Python development skills.
- Experience building and consuming REST APIs.
- Working SQL skills and comfort with data integration across systems.
- Experience with Git and collaborative development workflows.
- Comfort moving across multiple codebases/programs rather than owning a single one.
Preferred
- AI & Agentic Frameworks: LangGraph, CrewAI, Microsoft Semantic Kernls, AutoGen, OpenAI Agents Framework, Anthropic APIs, or Azure OpenAI Services.
- Domain: Exposure to channel marketing, co-op advertising, dealer/distributor platforms, or document/PDF-heavy workflows.
- Cloud & Tooling: Azure (Functions, AI Services, AKS), Docker, CI/CD pipelines, GitHub Copilot or other AI-assisted development tools.