Forward Deployed Engineer, Agentic AI
About the RoleRedapt is building dedicated capacity to design, deploy, and optimize agentic AI solutions for clients. We are looking for a Forward Deployed Engineer: a technical, client-facing builder who can turn AI capabilities into working production systems inside customer environments.
This role blends an embedded, hands-on delivery model with the broader technical advisory work Redapt clients need to get real ROI from AI investments. You will write production Python code, sit in front of clients to explain technical tradeoffs in plain language, and help both customers and internal Redapt teams get more value out of their existing AI workloads. This role is intentionally broader than a typical FDE position, combining embedded delivery with elements of technical advisory work, such as token and model optimization, AI security, and workload ROI, that support long-term client success.
What You'll Do Embedded Delivery & Engineering
- Embed directly with client teams to design, build, and ship production applications and integrations built on Claude models.
- Write and maintain production-quality Python code for AI integrations, agent workflows, and internal tooling.
- Set up and configure agentic harnesses (including Azure Foundry, AWS Bedrock, Claude Cowork-style deployments) so client teams can put AI to work on real business processes.
- Codify repeatable deployment patterns from client engagements and feed them back to the broader team so future engagements move faster.
Client Advisory & Stakeholder Engagement
- Present findings, architecture options, and recommendations directly to client stakeholders, translating technical concepts for both technical and non-technical audiences.
- Build durable, trusted relationships with client technical and business stakeholders over the life of an engagement.
- Work with internal teams to assess existing AI workloads and identify opportunities to improve ROI, whether through better architecture, tooling, or workflow redesign.
Model Optimization & AI Security
- Advise clients and internal Redapt teams on token optimization and model selection to control cost and improve performance.
- Evaluate and tune model configuration and prompting strategy to improve output quality and reliability for a given workload.
- Identify AI security considerations (data handling, access control, prompt injection, model governance) and build these into deployment recommendations.
Required Qualifications - 3+ years in a technical, client-facing role such as forward deployed engineering, solutions engineering, or consulting with a software engineering background.
- Strong Python skills, with experience shipping production code, not just prototypes or notebooks.
- Hands-on production experience with LLMs, including:
- Agent development
- Evaluation
- Deployment at scale
- Demonstrated ability to communicate clearly and credibly with clients, including in live, in-person settings.
- Working knowledge of Azure Foundry, AWS Bedrock, OpenAI/Codex and the Anthropic/Claude platform (Claude API, tool use, Claude Code, or similar).
- Comfort operating with ambiguity and owning a technical engagement end-to-end.
Preferred Qualifications - Experience with evaluation frameworks, RAG pipelines, or agent orchestration frameworks (e.g., LangGraph, LangChain, CrewAI).
- Familiarity with AI security, privacy, and governance considerations in enterprise environments.
- Experience with MLOps/LLMOps practices, including model monitoring and prompt management.
- Prior experience specifically with Azure Foundry, AWS Bedrock, or Anthropic's Claude platform (Claude for Enterprise, Claude Code, MCP).
- Background in enterprise IT systems or AI deployment patterns.
What Makes You Successful - You can translate deep technical work into plain language that lands with engineers and executives alike.
- You thrive working embedded inside client environments and take ownership of outcomes, not just recommendations.
- You are comfortable operating across the full arc of an engagement, from writing production code to advising on cost, security, and architecture.
- You enjoy turning one-off engagement learnings into repeatable patterns so the next client moves faster.
Technologies & Methodologies - Python
- OpenAI / Codex
- Anthropic / Claude API
- Agentic AI & Agent Development
- Azure AI Foundry
- AWS Bedrock
- AI Security & Governance
- RAG Pipelines (Preferred)
- LangGraph / LangChain / CrewAI (Preferred)
- MLOps / LLMOps (Preferred)
- MCP (Model Context Protocol) (Preferred)
Travel required: - Travel typically 5% or less a year
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