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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and deploying AI systems, with a focus on agentic AI architectures, compliance with governance and risk standards, and effective stakeholder communication. Proven ability to translate business objectives into scalable AI capabilities while ensuring system reliability and observability.
Highest-signal resume keywords
AI/LLM Application DevelopmentAgentic AI ExperienceGovernance, Risk, Compliance, and Audit ControlsSoftware Engineering FundamentalsStakeholder Communication
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Prototype to Production TransitionMulti-Agent CoordinationTool and Data IntegrationIdentity and Access ManagementSafety MechanismsTestable SystemsObservable SystemsMaintainable SystemsAI EvaluationPrompt/Context Design
Soft Skills
Excellent CommunicationStakeholder ManagementForward-Deployed MindsetComfort with AmbiguityIterative Delivery
Industry Keywords
AI Quality StandardsResponsible AIEmerging AI ModelsProduction-Grade SystemsAgent-Driven UI
About the role
Key responsibilities & impact- Take AI ideas from prototype to reliable, scalable, production-grade systems
- Design, implement, deploy, and evolve autonomous and tool-using agentic AI systems
- Build single-agent and multi-agent architectures with tool/function calling, MCP, A2A coordination, planning, reasoning, memory, identity/access, and safety controls
- Assess business problems and select appropriate models, agent patterns, and architectures
- Track emerging AI models, frameworks, techniques, and tooling; prototype and bring suitable technologies into production
- Ship software dependably within regulated-environment governance, compliance, risk, and audit controls
- Work directly with stakeholders to shape roadmaps and translate business goals into AI capabilities
- Define standards for AI quality, evaluation, observability, and responsible AI
- Serve as a trusted technical authority for AI delivery across the organisation
Requirements
What you’ll need- Building AI/LLM applications in production
- Proven ability to take ideas from prototype to hardened, scalable, maintainable production systems
- Proven track record delivering software to production in a regulated environment within governance, risk, compliance, and audit controls
- Hands-on experience with agentic AI, including multi-agent and agent-to-agent coordination, MCP, tool and data integration, and safety mechanisms
- Experience with identity and access for AI, delegated authority, service identities, authentication, authorization, and auditability
- Strong software engineering fundamentals, including reliable, observable, testable, and maintainable systems
- Excellent communication and stakeholder skills
- Forward-deployed mindset, comfortable with ambiguity and iterative delivery
- Strongly desirable: RAG, AI evaluation, guardrails and safety, prompt/context design, memory, architecture leadership, and emerging technology
- Nice to have: customer-facing AI and agent-driven or AI-generated UI