AI Engineer / AI Analyst
Description
The AI Engineer/Analyst is a dual discipline role operating across two integrated workstreams. On the engineering side, you will evaluate emerging AI tools, platforms, and agents, conduct structured technical assessments, build and integrate AI workflows and services into enterprise systems, and contribute to the MCP platform build including guardrails, permissions, and agent architectures. Working closely with Architecture, Security, DevOps, Legal, Privacy, and business stakeholders, you will ensure AI initiatives are technically sound, properly governed, and delivering measurable business value.
Responsibilities
- Evaluates emerging AI tools, platforms and agents
- Coordinates the structured progression of AI initiatives through defined technical evaluation and approval stages, ensuring required documentation, testing artifacts, and governance reviews are completed and properly recorded
- Develops evaluation plans, overseeing user acceptance testing, compiling evidence packs for approvals and audits, and maintaining detailed registries of AI tools, use cases, and lifecycle records
- Monitors adoption metrics, cost and consumption trends, quality indicators, and post-release performance, escalating risks or anomalies
- Supports stakeholder alignment across technical, security, legal, and business teams to drive consistent and compliant AI adoption.
- Demonstrates strong cross-functional communication and collaboration skills, with the ability to adapt to a rapidly evolving AI landscape and a proactive commitment to continuous learning in enterprise AI governance.
Requirements
- 5+ years of combined experience across software engineering, AI/ML development, business analysis, governance, risk/compliance, or data/operations roles in enterprise environments.
- Hands-on software development experience in Python and/or TypeScript/Node.js, with proven ability to build backend services, APIs, and integrations with enterprise systems (authentication, authorization, logging, monitoring).
- Hands-on GenAI/LLM application experience including agents, tool/function calling, RAG architectures, embeddings, vector search, and prompt engineering.
- Hands-on experience with enterprise GenAI platforms and foundation models including ChatGPT/OpenAI, Claude/Anthropic, Microsoft Copilot, Google Gemini, and cloud AI services such as AWS Bedrock, Azure AI Foundry, or equivalent multi-model environments.
- Proven experience in Generative AI solution development and virtual agent orchestration, including agentic workflows, multi-agent systems, conversational AI design, and AI-assisted automation for enterprise processes.
- Demonstrated ability to turn ambiguous requests into clear requirements, acceptance criteria, and measurable success metrics for both technical deliverables and governance artifacts.
- Experience coordinating testing and UAT; comfort building structured evaluation artifacts (test plans, expected behaviours, defect triage, rubric-based LLM evaluation).
- Strong documentation discipline and attention to detail; ability to produce audit-ready evidence packs, risk scoring records, and executive-ready reporting.
- Working knowledge of governance and control concepts (data classification, privacy/security reviews, access controls, change control, third-party/vendor risk).
- Solid DevOps fundamentals: Git-based workflows, CI/CD, containerization (Docker), and cloud deployment patterns.
- Strong security mindset: secrets management, encryption, audit logging, secure API design; familiarity with LLM-specific threats and mitigations.
- Excellent stakeholder management: able to drive follow-through across Security, Legal, Architecture, Engineering, and business teams.
