Director- Enterprise AI Platform

Role: Director of Engineering Enterprise AI Platform

Location : 100% Remote

Job Type: Fulltime

Core Qualifications

• Deep proficiency in Python and/or TypeScript, with a track record of writing clean, testable, productionquality code in modern engineering environments.
• Handson experience building LLM applications, including agent development, prompt engineering, and AI workflow automation (e.g., tools, function calling, orchestration).
• Demonstrated technical leadership across multiple concurrent projects or teams, including mentoring engineers and driving best practices for GenAI delivery.
• Experience designing and implementing REST API integrations in complex enterprise environments (authentication, ratelimiting, observability, error handling).
• Strong capability in producing highquality technical and architectural documentation for architecture review boards and other governance forums.
• Proficiency using AIpowered coding assistants (e.g., GitHub Copilot, Cursor, Claude Code) as everyday development tools to increase productivity and code quality.
• Solid understanding of software engineering fundamentals: version control (Git), CI/CD, automated testing, code reviews, and production support practices.
• Comfort working in Agile product teams, collaborating closely with product managers, designers, and stakeholders to translate business requirements into AI solutions.

Preferred Qualifications

• Experience with two or more enterprise AI/cloud platforms such as AWS (Bedrock/SageMaker), Azure OpenAI, Google Cloud Platform/Vertex AI, IBM Watsonx, Writer, Copilot Studio, or similar enterprisegrade AI stacks.
• Handson experience designing and operating productiongrade RAG pipelines, including vector stores, embedding models, chunking strategies, evaluation frameworks, and runtime monitoring/observability.
• Experience with multiagent systems or orchestration frameworks (e.g., LangChain, LlamaIndex, CrewAI, LangGraph, or vendornative SDKs) to build complex workflows and toolusing agents.
• Proven track record of building reusable engineering assets (SDKs, internal libraries, templates, reference architectures) that are adopted by other teams and improve delivery velocity.
• Familiarity with enterprise governance and compliance workflows, including architecture and security reviews, risk assessments, and data privacy/regulatory considerations.
• Experience with LLM evaluation and optimization: prompt testing, guardrails, safety policies, latency and cost optimization, and A/B testing in production.
• Exposure to MLOps/LLMOps practices such as model deployment, versioning, experimentation, and observability for LLMdriven systems.
• Background in related domains such as search/retrieval systems, knowledge management, or recommendation systems is a plus.