Agentic AI Developer IV

• Work directly with engineering teams, champions, and internal stakeholders to help them adopt AI effectively.

include:

• Pairing with teams on AI use cases and implementation patterns Providing technical guidance on LLM, RAG, and agentic workflow design Supporting proof-of-concept efforts and helping mature them into repeatable practices Creating playbooks, examples, templates, and documentation for internal engineering use Participating in office hours, workshops, and AI enablement sessions
• Pairing with teams on AI use cases and implementation patterns
• Providing technical guidance on LLM, RAG, and agentic workflow design
• Supporting proof-of-concept efforts and helping mature them into repeatable practices
• Creating playbooks, examples, templates, and documentation for internal engineering use
• Participating in office hours, workshops, and AI enablement sessions
• AI Evaluation, Quality, and Responsible Use
• Help define and apply practical evaluation and governance practices for internal AI solutions, including: Prompt and workflow evaluation Accuracy, relevance, and usefulness testing Safety and responsible AI considerations PII and sensitive-data handling Logging, observability, and feedback loops Human-in-the-loop review patterns where appropriate
• Prompt and workflow evaluation
• Accuracy, relevance, and usefulness testing
• Safety and responsible AI considerations
• PII and sensitive-data handling
• Logging, observability, and feedback loops
• Human-in-the-loop review patterns where appropriate
• Ensure internal AI solutions are developed with quality, security, privacy, and reliability in mind.
• Delivery and Cross-Functional Collaboration
• Partner with engineering leadership, product teams, architecture, security, and other stakeholders to identify and deliver high-impact AI use cases.

include:

• Translating engineering productivity needs into AI-enabled solutions Supporting roadmap-aligned internal AI initiatives Contributing to adoption and capacity-improvement goals Helping measure the impact of AI enablement efforts Communicating technical concepts clearly to engineering and non-engineering audiences
• Translating engineering productivity needs into AI-enabled solutions
• Supporting roadmap-aligned internal AI initiatives
• Contributing to adoption and capacity-improvement goals
• Helping measure the impact of AI enablement efforts
• Communicating technical concepts clearly to engineering and non-engineering audiences

• Design AI solutions with practical performance and cost considerations, including: Model selection and routing Prompt and context optimization Caching and retrieval efficiency Latency and reliability considerations Build-vs-buy recommendations Avoidance of vendor lock-in where practical
• Model selection and routing
• Prompt and context optimization
• Caching and retrieval efficiency
• Latency and reliability considerations
• Build-vs-buy recommendations
• Avoidance of vendor lock-in where practical
• Typically 6+ years of software engineering experience , with meaningful hands-on experience building production applications or internal platforms.
• 2+ years of applied AI, LLM, Generative AI, or agentic workflow experience .
• Strong programming experience in Python , TypeScript/JavaScript , or similar production languages.
• Experience designing and building cloud-native applications or services in Azure, GCP, or AWS .
• Practical experience with:

LLM-based application development

Prompt engineering and prompt versioning

Tool calling / function calling

RAG architectures

Vector databases or semantic retrieval

Multi-step workflow or agent orchestration

• Familiarity with modern software engineering practices, including:
• CI/CD
• Git-based development
• Automated testing
• API design
• Observability and logging
• Experience using or enabling AI coding tools such as GitHub Copilot, Cursor, Windsurf, Codex, or similar tools .
• Ability to work directly with engineering teams to understand needs, prototype solutions, and drive adoption.
• Strong communication skills with the ability to explain AI concepts and implementation patterns clearly.

• Experience building internal developer platforms, engineering productivity tools, or enablement frameworks.
• Experience with agent frameworks or orchestration tools such as LangGraph , OpenAI Agents SDK, Google ADK, Semantic Kernel, CrewAI , or similar frameworks .
• Experience with evaluation frameworks such as OpenAI Evals, LangSmith Evals, RAGAS, or custom evaluation harnesses .
• Experience with browser automation or workflow automation tools such as Playwright .
• Experience with knowledge management, internal documentation systems, or enterprise search.
• Experience working in environments with privacy, compliance, or regulated-data considerations.
• Background in enterprise software, PropTech , fintech, or other complex business domains.
• Experience supporting AI adoption programs, engineering champions, office hours, or internal technical enablement.
• Health, dental, and vision insurance.
• Retirement savings plan with company match.
• Paid time off and holidays.
• Professional development opportunities.
• Performance-based bonus based on position. #LI-JK1 #LI-REMOTE
• #LI-JK1 #LI-REMOTE