Senior Forward Deployed AI Engineer
Senior engineer on Bell's forward-deployed Applied AI team, embedding with business squads to scope problems and ship production-grade AI agents and automation (multi-agent systems, MCP servers, RAG pipelines) integrated into enterprise platforms like Google Cloud, Amazon Bedrock, Salesforce, and ServiceNow, while mentoring a junior engineer. Hybrid role based in Mississauga, Toronto, or Montreal.
- Compensation
- Not disclosed
- Location
- Mississauga, CA
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Full time
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Skills
Must have
- Production experience building agentic applications: agent skills and tools, MCP servers, and multi-agent workflows, from prototype to production
- Experience designing and deploying AI systems on a major cloud platform (e.g., Google Cloud)
- Experience building data pipelines over structured and unstructured data, using vector databases and RAG architectures for enterprise AI
- Strong software engineering foundation, including Python, APIs, and integration with production enterprise systems
- Habit of staying current with fast-moving LLM and agent capabilities, patterns, and tooling
- Adequate knowledge of French required for positions in Quebec
Nice to have
- 5 to 10 years in software engineering, including 2 to 3 years hands-on with GenAI/agents
- Professional experience at a hyperscaler or startup considered an asset
- Experience automating on enterprise platforms (Google Cloud, Amazon Bedrock) and familiarity with Google, Salesforce, ServiceNow
- Open-source contributions or published work in the agent or LLM space
- Proven record shipping AI products with measurable financial outcomes
- 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
Full description
Req Id: 431272
Connection is everything. It drives us to innovate, explore, and stay close to what matters to us most. At Bell, we’re building a more connected future through world-class networks, AI-powered solutions, and digital experiences that elevate how people live, work, and play every day.
We believe in empowering people. That’s why we equip our teams with cutting-edge technology, AI tools, and a collaborative environment that supports creativity and growth. Want to be part of a diverse team where your work makes a real impact? If you’re inspired by innovation that advances how people connect and transforms what’s possible, you belong on #TeamBell.
Become an integral part of our Customer Experience team, where you will be at the forefront of shaping our customer journeys. You will build on Bell’s Strategic Imperative to Champion Customer Experience and reinforce our commitment to put the customer first in everything we do. Design innovative, customer-centric processes, policies, products and services, while upholding our Customer-First Commitments to make it easier for customers to do business with Bell.
As part of our team, you will be at the heart of our customer experiences.
Summary
The Technology Services Applied AI team is a forward-deployed engineering team that embeds across Bell to build, ship, and integrate AI agents and automation into existing enterprise systems, and drives the innovation and build funnel for agentic software development.
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
- Embed into transformation squads and lead agentic software development builds, owning each engagement end-to-end.
- 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.
- 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).
- Recognize when a use case requires bespoke modelling and route it to the central data science team.
- Mentor and pair with an early-career engineer on the engagement.
- Depending on experience, own the team's evaluation standards and quality bar.