About Channel Fusion

Founded more than 20 years ago, Channel Fusion provides brands and their channel partners with an ecosystem of channel marketing technologies, solutions and services. By coupling innovative technologies, industry expertise and a relentless customer-focused support team, we provide a unique combination of platforms, products and people.

Our purpose is simple: inspire our clients to achieve their desired outcomes by transforming channel marketing into impactful business results. We do this by ensuring an understanding of your desired outcomes first and then deploying tailored solutions using a disciplined process to achieve those outcomes.

Once a program is operational, our support team of Fusers becomes an extension of your brand to ensure your channel partners have an optimal customer experience while maximizing their marketing investment in your brand. Our account leadership teams stay involved every step of the way to ensure programs continue to exceed expectations and drive your desired outcomes.

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 role leads a small team responsible for architecting and delivering autonomous agents. Field Product Managers (FPMs) define the product features and capabilities the agents need to support and what the agent needs to accomplish and for which client/program. This team owns how that gets architected and delivered: orchestration design, tooling, agent standards, evaluation, and production reliability.

Because AI agents also show up inside engineering workflows owned by other Lead Developers (e.g., code generation, testing, delivery automation), this role coordinates closely with those Leads so agent implementations inside engineering are built to the standards and requirements those Leads set for their own domains and this role is not overriding others Leads' engineering requirements, it's the shared agent-implementation capability they draw on.

What You Own

Agent architecture & Delivery. Translate FPM defined product features/capabilities into agent architecture, orchestration design, and delivery plans.
Technical Design. Design multi-agent systems, tool-calling patterns, memory/orchestration approaches, and reusable agent frameworks used across teams.
Team Leadership. Lead and mentor the small team of engineers implementing agents, review designs, unblock technical decisions, set coding/agent-eval standards for the team.
Coordination with Engineering Leads. Ensure agents built for engineering workflows (e.g., CI/CD automation, code review agents) meet the requirements set by the relevant Lead Developer for that domain. This role builds it and the domain Lead sets the bar.
Operational Quality. Build monitoring, loggin, and evaluation frameworks, own token/cost efficiency and reliability of shipped agents.

What You Do NOT Own

Product Scope & Requirements. What the agent needs to accomplish and for which client/program which is owned by the Field Product Manager.
Engineering Domain Requirements. Requirements or acceptance criteria for engineering-embedded agents (e.g., a code-review agent) and owned by the relevant Lead Developer for that workstream; this role implements to their spec.
AI Governance Framework. Program-wide AI governance and the ADLC framework is owned at the TSL Director level; this role operates within that framework rather than setting it.
Client Relationship & Roadmap Prioritization. Owned by the CSM/FPM for their accounts, informed by patterns this team observes in implementation.

Key Responsibilities

Agentic Workflow Development

Build intelligent agents that automate engineering, product, and support, and operational workflows against FPM-defined requirements.
Develop multi-agent systems capable of planning, reasoning, and task execution.
Create reusable agent frameworks and libraries that accelerate development across teams.
Build workflow automation using tool calling, memory, and orchestration patterns.

Enterprise AI Integration

Integrate AI solutions with internal and external business systems.
Develop secure API and data access frameworks.
Create retrieval-augmented generation (RAG) systems leveraging enterprise knowledge.
Design scalable architectures supporting both internal and customer-facing AI capabilities.

Team Leadership

Lead and mentor a small team of AI engineers delivering against the FPM-defined roadmap.
Set coding standards, design patterns, and agent-engineering best practices for the team.
Run architecture reviews and technical design sessions.
Coordinate with other Lead Developers to align on standards for any agents touching shared engineering systems.

Dev Ops & Operations

Build monitoring, logging, evaluation, and performance frameworks for AI systems.
Optimize model utilization, token consumption, and overall platform costs.
Implement CI/CD pipelines for AI applications and agent deployments.
Ensure security, compliance, reliability, and maintainability of AI services.

Qualifications

Required

5+ years of professional software engineering experience.
2+ years building production AI-enabled applications.
Experience designing cloud-native distributed systems.
Prior experience leading a small team or serving as a technical lead on a delivery team.
Experience building and consuming REST APIs.
Expert-level Python development.
Strong understanding of software architecture and design patterns.
Experience with modern cloud platforms (Azure strongly preferred).
Experience with containerization technologies such as Docker.
Experience with CI/CD pipelines and DevOps practices.
Strong SQL and data integration capabilities.
Experience with Git and collaborative software development workflows.

Preferred

AI & Agentic Frameworks: LangGraph, CrewAI, Microsoft Semantic Kernel, AutoGen, OpenAI Agents Fraemwork, Anthropic APIs, or Azure OpenAI services.
Agentic System Development: Multi-agent orchestration systems, tool calling workflows, RAG, prompt management frameworks, knowledge retrieval systems, autonomous workflow automation, eveluation/benchmarking frameworks.
Cloud & Infrastructure: Azure Functions, Azure AI Services, Azure Kubernetes Services (AKS), Azure API Management, event-driven architectures, observability/monitoring platforms.
Modern Engineering Practices: GitHub Copilot, AI-Assisted software development, test automation, Infrastructure as Code, secure SDLC practices, DevSecOps methodologies.

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Channel Fusion

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