Senior Manager

Key Responsibilities

1. Rapid Prototyping & Application Development

Build AI applications, copilots, and agentic workflows end-to-end – UI, APIs, business logic, and model integration.
Use rapid development tools (Cursor, Claude Code, Replit, Google AI Studio) to compress build cycles and iterate quickly with users and stakeholders.
Turn loosely-defined requirements into working demos and prototypes within days, then refine based on feedback.

2. Agentic & GenAI Engineering

Develop with agentic SDKs and frameworks – OpenAI Agents SDK, Anthropic Claude (Agent SDK / API), Google Gemini & ADK, LangChain/LangGraph.
Implement RAG pipelines, tool/function calling, structured outputs, and prompt engineering with systematic testing and evals.
Integrate models and agents with enterprise data sources and APIs, handling auth, rate limits, and error paths properly.

3. Engineering Quality & Productionization

Write clean, testable, well-documented code; use Git, containers, and CI/CD as standard practice.
Partner with Forward Deployment Engineers and platform teams to take successful prototypes into production, adding monitoring, guardrails, and cost controls.
Balance speed and quality pragmatically – knowing when to hack and when to harden.

4. Collaboration & Continuous Learning

Work closely with architects, data scientists, and designers; contribute to demos, accelerators, and internal hackathons.
Stay current with the fast-moving model and tooling landscape, and share learnings across the team.
Evangelize AI-assisted development practices that raise the whole team’s velocity.

Technical Skills & Tooling (Hands-On)

Rapid development tools as daily drivers: Cursor, Claude Code, Replit, Google AI Studio, GitHub Copilot – demonstrated ability to ship real software with AI-assisted workflows.
Agentic SDKs & frameworks: hands-on experience with OpenAI Agents SDK, Anthropic Claude APIs/Agent SDK, Google Gemini/ADK, and LangChain or LangGraph.
Strong programming skills in Python and/or TypeScript/JavaScript; comfort building full-stack prototypes (React/Node) and REST APIs.
LLM application patterns: prompt engineering, function/tool calling, structured outputs, RAG with vector stores (pgvector, Pinecone, FAISS, or similar).
Testing & observability basics: writing evals, using tracing tools (LangSmith, Langfuse, or similar), and monitoring cost/latency/quality.
Engineering foundations: Git, Docker, CI/CD, and at least one cloud (AWS/Azure/GCP).
Good to have: voice/multimodal experience (ElevenLabs, HeyGen), MCP-based tool integration, fine-tuning or open-source LLM experience.

Speed of delivery: consistent idea-to-prototype turnaround in days and prototype-to-production in weeks.
Volume and quality of shipped work: applications, demos, and accelerators that are actually used by stakeholders and internal teams.
Reliability of what ships: low defect rates, sensible test/eval coverage, and predictable cost/latency behavior.
Contribution to reuse: components, patterns, and utilities adopted by other engineers.

Team velocity uplift through shared AI-assisted development practices.

4–8 years of software engineering experience, with 1–2+ years building GenAI/LLM applications hands-on.
A portfolio of shipped AI work – products, prototypes, GitHub projects, or demos you can walk us through.
Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent practical experience).
Demonstrated fluency with AI-native development tools (Cursor, Claude Code, Replit, AI Studio) in real projects – not just experimentation.
Strong problem-solving skills and product sense – you care about whether the thing you built actually gets used.

Clear written and verbal communication; comfortable demoing your work to technical and business audiences.

See also