Senior Agentic AI Engineer

Citi•Mississauga, ON

1d•CA$120,800 - CA$170,800

About The Position

We are seeking an experienced Senior Generative AI Developer to help drive the design, development, and integration of state-of-the-art Generative AI and agentic AI solutions across our enterprise Controls Technology platform. You will collaborate with cross-functional teams, contribute deep technical expertise in context engineering, retrieval systems, knowledge graphs, and multi-agent orchestration, and play a key role in delivering scalable, grounded AI solutions to enhance automation and operational efficiency. This role centers on architecting robust applications and agent systems on top of pre-trained and hosted foundation models — not on training or fine-tuning models.

Requirements

6+ years of experience in AI/software development, including significant experience in Generative AI and agentic AI.
Strong skills in Python and experience with data preprocessing, document ingestion, and API development.
Deep, hands-on expertise in core generative AI concepts — foundation models, LLMs, embeddings, tokenization, and context-window management.
Proficiency with major GenAI APIs (OpenAI, Gemini, Claude, etc.) and orchestration frameworks such as LangChain and LlamaIndex.
Advanced skills in prompt engineering and context engineering, including familiarity with prompt design tools/frameworks and dynamic context orchestration.
Proficiency with vector databases and embedding models for large-scale retrieval.
Strong experience building RAG systems, including chunking strategies, hybrid search, and multi-vector retrieval.
Practical experience designing knowledge graphs and Graph RAG pipelines (e.g., using graph databases such as Neo4j or ArangoDB) for relationship-aware, multi-hop retrieval.
Proven experience building agentic AI systems with Google ADK and/or comparable frameworks (LangGraph, Microsoft Agent Framework, CrewAI, OpenAI Agents SDK), including tool/function calling, planning, and memory.
Strong grasp of multi-agent orchestration patterns (supervisor/worker, hierarchical, peer-to-peer) and harness engineering (governance, feedback loops, execution controls, agent isolation/sandboxing).
Hands-on experience with agent interoperability protocols — the Model Context Protocol (MCP) for tool/data access and the Agent2Agent (A2A) protocol for inter-agent collaboration.
Experience with agent observability and evaluation (e.g., tracing, OpenTelemetry-based tooling) for production agent systems.
Experience working with AWS (or equivalent) cloud infrastructure for AI/GenAI.
Experience with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines for AI/agentic applications.
Strong skills in NLP (NER, dependency parsing, text classification, topic modeling).
Solid understanding of AI compliance, guardrails, and responsible AI practices.
Demonstrated portfolio of successful AI-driven projects in a business environment.
Strong collaboration skills to work effectively in cross-functional teams.
Analytical and proactive approach to problem-solving.
Clear communication skills for both technical and non-technical audiences.
Eagerness to learn, innovate, and mentor less experienced developers.

Nice To Haves

Master’s degree preferred

Responsibilities

Collaborate with AI architects, leads, and stakeholders to design and implement generative and agentic AI solutions that address business challenges.
Architect advanced context engineering strategies — context layering, chaining, compression, pruning/offloading, and memory management — to maximize reliability, provenance, and token efficiency in production.
Design and implement advanced generative AI methods, including sophisticated prompt engineering and Retrieval-Augmented Generation (RAG).
Build and optimize RAG systems, including hybrid search, multi-vector retrieval, and re-ranking pipelines.
Design and implement knowledge graphs and Graph RAG architectures to enable multi-hop reasoning, explainability, and traceable, grounded responses for high-value business domains.
Architect agentic workflows and multi-agent systems using Google Agent Development Kit (ADK) and comparable frameworks (LangGraph, Microsoft Agent Framework, CrewAI), applying orchestration patterns such as supervisor/worker, hierarchical, and peer-to-peer.
Design robust agent harnesses — governance, constraints, feedback loops, state/session management, and execution controls that make long-running agent systems reliable and safe.
Integrate agents with tools and data via the Model Context Protocol (MCP) and orchestrate inter-agent collaboration and task delegation via the Agent2Agent (A2A) protocol.
Support the integration of GenAI and agentic applications into production environments, ensuring robust deployment, scalability, observability, and maintainability.
Contribute to the development and optimization of real-time and streaming AI solutions.
Stay current with the latest advances in generative and agentic AI and actively share knowledge with the team.
Ensure adherence to ethical AI guidelines, guardrails, agent isolation/sandboxing, data privacy, and compliance standards.
Mentor junior team members, provide code reviews, and foster a culture of technical excellence.