The mission of the AI Enablement Engineer is to accelerate the adoption and effective use of AI across the organization's engineering teams. This role evaluates, develops, and integrates AI solutions to improve developer productivity while providing the infrastructure, tools, and services needed to build AI-powered products.
Responsibilities
Evaluate, select, and integrate AI models, frameworks, and tools.
Design, develop, and deploy AI-powered services and solutions.
Implement RAG, AI Agents, and automation workflows.
Develop APIs, SDKs, and internal tools for engineering teams.
Build and maintain self-hosted AI platforms and infrastructure.
Enable engineering teams to effectively use AI throughout the Software Development Life Cycle (SDLC).
Collaborate with Product, Engineering, and Platform teams to build AI capabilities.
Requirements
Technical Skills
At least 3 years of professional software development experience.
Proficiency in one or more of Go, Java, JavaScript/TypeScript, or Python.
Strong experience in backend development, API design, and software architecture.
Experience with Docker, Kubernetes, CI/CD, and modern deployment practices.
Familiarity with distributed systems and scalable software design.
AI Skills
Strong understanding of Large Language Models (LLMs), embeddings, prompt engineering, context engineering, and structured outputs.
Experience building AI Agents, Retrieval-Augmented Generation (RAG), Tool Calling, Function Calling, and AI automation workflows.
Experience integrating external tools and services using the Model Context Protocol (MCP) or similar protocols.
Experience with frameworks such as LangGraph, LangChain, LlamaIndex, or equivalent.
Experience working with both commercial and open-source AI models.
Experience designing AI evaluation, benchmarking, and regression testing pipelines.
Understanding of AI safety, guardrails, and responsible AI practices.
AI Platforms, Tooling & Infrastructure
Experience designing, developing, and maintaining internal AI platforms.
Experience deploying and managing AI models and model providers.
Experience building APIs, SDKs, and shared AI services for engineering teams.
Familiarity with self-hosted AI platforms and technologies such as Ollama, vLLM, LiteLLM, or similar.
Experience working with vector databases and AI infrastructure.
Familiarity with technologies such as Redis, Kafka, PostgreSQL, ClickHouse, MinIO, or similar.
Experience implementing AI observability, logging, monitoring, and tracing.
Understanding of performance optimization, capacity planning, and cost management for AI services.
Knowledge of authentication, authorization, security, and governance for AI platforms.
Nice to Have
Experience integrating AI into developer workflows and SDLC.
Familiarity with AI coding assistants such as Cursor, Claude Code, GitHub Copilot, or similar tools.
Experience with OpenRouter or AI gateway solutions.
Experience contributing to internal developer platforms or engineering productivity initiatives.
Experience designing reusable AI components, prompt libraries, and internal AI frameworks.
Familiarity with OpenTelemetry and modern observability platforms.
Experience building or operating production AI systems at scale.
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