Senior AI Agentic Engineer

Senior AI Agentic Engineer

The Senior AI Agentic Engineer is a senior production builder responsible for designing, implementing, testing, and operating enterprise-grade agentic systems that solve real business problems s role sits at the center of the Forge delivery model, translating approved architecture patterns into production workflows that combine prompts, tools, APIs, memory, retrieval, orchestration logic, and human oversight into working enterprise capabilities. This is a hands-on senior engineering role focused on agent workflows, multi-step reasoning, prompt architecture, tool integration, memory/context management, retrieval-augmented generation (RAG), evaluation, observability, and governed deployment.

The engineer works across platforms and frameworks used to deliver production AI capabilities, including Microsoft and Azure-based agent platforms, enterprise workflow tooling, and other approved agentic engineering stacks where needed. Daily work includes building agent flows, implementing orchestration logic, integrating APIs and enterprise systems, tuning prompts and execution patterns, validating output quality, hardening reliability, instrumenting observability, and supporting secure deployment through enterprise release controls.

The role is expected to operate with senior-level ownership, production judgment, and deep technical accountability while still moving at startup speed inside enterprise governance.

Essential Duties and Responsibilities

Following is a summary of the essential functions for this job. Other duties may be performed, both major and minor, which are not mentioned below. Specific activities may change from time to time.

• Design, build, and deploy production-ready AI agents and agentic workflows that combine prompts, tools, APIs, memory, retrieval, and business logic into useful enterprise capabilities.
• Implement single-agent and multi-agent workflows using approved Forge patterns, including tool orchestration, supervisor-worker patterns, human-in-the-loop controls, session-aware execution, and safe fallback behaviors.
• Translate architecture and product intent into robust implementations that are observable, testable, secure, and maintainable in enterprise production environments.
• Build and maintain integrations to enterprise systems, APIs, workflow services, and governed data sources that enable agents to act safely and effectively within approved boundaries.
• Design and maintain prompt architectures, tool schemas, retrieval logic, memory behavior, and structured execution controls that improve agent accuracy, reliability, and explainability.
• Contribute to RAG, grounding, and knowledge-retrieval patterns using search, vector stores, document pipelines, and governed enterprise data services.
• Implement runtime controls for resilience and safety, including error handling, retries, tool constraints, structured outputs, human escalation, and observable execution behavior.
• Support evaluation and regression validation for agentic solutions, including prompt changes, model changes, workflow changes, and quality benchmarks tied to business outcomes.
• Use approved engineering acceleration tools to speed code generation, testing, refactoring, and documentation while remaining accountable for production quality and correctness.
• Write and maintain automated tests, integration validations, deployment-readiness checks, and runbooks for agent-enabled solutions and their supporting services.
• Partner with architecture, security, QA, product, data, platform, and enablement teams to move AI capabilities from design into reliable production delivery.
• Contribute reusable engineering patterns, reference implementations, and technical guidance that raise delivery quality and speed across the broader Forge engineering organization.

Additional Needs

• 5+ years of software engineering experience, including strong hands-on experience building and supporting production systems in enterprise environments.
• Strong programming ability in Python and working knowledge of Type Script, JavaScript, or another modern language commonly used in enterprise engineering stacks.
• Demonstrated experience building AI-enabled applications, prompt-driven workflows, orchestration logic, or agentic systems that move beyond simple chat responses.
• Experience integrating APIs, enterprise systems, workflow services, tools, and governed data sources into applications or automations with production-grade reliability.
• Strong understanding of prompt design, tool-calling, workflow branching, context handling, and engineering patterns required to make AI systems dependable in production.
• Experience with automated testing, CI/CD, code review, observability, and release controls in modern engineering organizations.
• Ability to work across architecture, implementation, reliability, security, and operational concerns instead of only isolated coding tasks.
• Strong written and verbal communication skills and the ability to operate effectively in cross-functional…