AI Platform Engineer

Job Responsibilities

• Platform & Agentic Engineering: Build and maintain enterprise GenAI capability pipelines, agentic workflows, and Model Context Protocol (MCP) integrations using Python, AWS native services (Bedrock, EKS, Lambda), and vector/graph databases.
• Governance & Control Implementation: Partner with Infosec, Risk, and Legal teams to translate compliance requirements into automated technical controls (IAM policies, content filtering, audit logging, rate limiting).
• FinOps & Usage Monitoring: Manage token budgets, API quotas, and spend transparency by building automated tracking dashboards, anomaly detection, and cost guardrails.
• Developer Support & Enablement: Create self-service documentation, runbooks, and onboarding guides to assist engineering teams with platform adoption, prompt applications, and skill integrations.

Job Description:

• Experience: 6+ years in software engineering, with 1–2+ years dedicated to AI platforms, cloud enablement, or emerging technologies.
• AI/LLM Stack: Hands-on experience with GenAI models (GPT, Claude, LLaMA), agentic frameworks, MCP servers, and evaluation tools (e.g., Ragas).
• Cloud & DevOps: Proficiency with AWS services (Bedrock, EKS, Fargate, Lambda, Athena), Infrastructure as Code (Terraform), and containerization (Docker).
• Data & Tooling: Strong Python skills (Boto3, Pandas) and experience with vector or graph databases (PGVector, Milvus, Neo4j, Neptune).
• Governance & Soft Skills: Track record of enforcing DevSecOps best practices and collaborating effectively across cross-functional security, risk, and business stakeholders.

Education:

• Bachelor's or Master’s degree in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field. 

Note: GlobalLogic estimates the starting pay range for this role to be performed in Toronto, ON to be $CAD 140,000 to $145,000 and reflects base salary only. This pay range is provided as a good-faith estimate, and the amount offered may be higher or lower. GlobalLogic takes many factors into consideration in making an offer, including candidate qualifications, work experience, operational needs, travel and onsite requirements, internal peer equity, prevailing wage, responsibilities, and other market and business considerations.