Senior Forward-Deployed AI Engineer 1

Summary

The Technology Services Applied AI team is a forward-deployed engineering team that embeds across Bell to build, ship, and integrate classical ML, AI agents, and automation into existing enterprise systems.
As a Senior Forward-Deployed AI Engineer, you will own engagements end-to-end: scoping the problem, choosing the right approach across automation, classical ML, and GenAI/agents, and taking solutions from rapid prototype to production-grade agentic workflows (such as multi-agent systems and Model Context Protocol servers) that deliver measurable ROI. Along the way, you will mentor a paired early-career engineer.

Key Responsibilities

• Scope the problem with business partners and decide how to solve it: the right approach across rules, automation, classical ML, and GenAI/agents, and where the solution should live across the systems landscape.
• Partner with stakeholders to produce solutions with high adoption rates and scalability for ease of growth and reusability across business domains.
• Embed into cross-unit squads and lead application builds, owning each engagement end-to-end.
• Design, build, and ship production-grade AI agents and automation, integrating with existing enterprise systems and platforms (e.g., Google Cloud, Amazon Bedrock, Salesforce, ServiceNow).
• Mentor and pair with an early-career engineer on the engagement.
• Depending on experience, own the team's evaluation standards and quality bar.

Critical Qualifications

• 4-7 years in software engineering, including 2 to 3 years hands-on with GenAI/agents.
• Experience automating on enterprise platforms (Google Cloud, Amazon Bedrock) and familiarity with our key experience platforms (Google, Salesforce, ServiceNow).
• Open-source contributions or published work in the agent or LLM space.
• Proven record shipping AI products with measurable financial outcomes (e.g., cost reductions, productivity gains, etc.)
• Experience setting up evaluation and observability for agentic systems.
• Prior client-facing, consulting, or forward-deployed delivery experience.
• Exposure to MLOps/LLMOps for deploying and monitoring in production.

Preferred Qualifications

• Experience designing and deploying AI systems on a major cloud platform (e.g., Google Cloud).
• Production experience building and iterating upon agentic applications and the capabilities that power them: agent skills and tools, Model Context Protocol (MCP) servers, and multi-agent workflows, taken from prototype to production.
• Experience building data pipelines over structured and unstructured data, using vector databases and retrieval-augmented (RAG) architectures for enterprise AI.
• Strong software engineering foundation, including Python, APIs, and integration with production enterprise systems.
• A habit of staying current with fast-moving LLM and agent capabilities, patterns, and tooling.