Forward Deployed Engineer, Agentic AI

Summary

Redapt is building dedicated capacity to design, deploy, and optimize agentic AI solutions for clients. The Forward Deployed Engineer will embed with customer teams to build production AI applications, write Python integrations, configure agentic platforms, and provide client-facing guidance on architecture, optimization, security, and ROI.

Responsibilities

• 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
• 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
• 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

Skills

• 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
• Travel typically 5% or less a year
• 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

Qualifications

Must Haves

• 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
• Travel typically 5% or less a year

Nice to Haves

• 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