Forward Deployment Engineer

Job Description:

Role Overview

We are looking for Forward Deployment Engineers (FDEs) to work directly with customer business and technology teams to design, build, and deploy AI-driven solutions that transform real-world workflows.

This is a hybrid role requiring both product thinking (problem discovery, requirements, success metrics) and hands-on engineering (rapid prototyping through to production deployment). The ideal candidate is comfortable operating under ambiguity, embedding customer teams, and delivering measurable outcomes using AWS, Python, and Claude (and related AI/agent stacks).

Key Responsibilities

1) Product Discovery & Solution Shaping (PM capability)

Partner with customer stakeholders to understand current workflows, pain points, constraints, and desired outcomes.

Translate business objectives into a clear delivery plan: requirements, user stories, acceptance criteria, and roadmap from prototype to scaled deployment.

Define success metrics (e.g., cycle-time reduction, automation rate, accuracy, adoption) and drive alignment through regular demos and updates.

2) Forward-Deployed Execution (Client-embedded delivery)

Embed with client teams to whiteboard solutions, build prototypes quickly, deploy, and iterate based on feedback.

Own outcomes end-to-end from discovery to production release, stabilization, and continuous improvement.

Troubleshoot issues in real environments and ensure solutions are reliable, maintainable, and aligned to business needs. 3) Hands-on Engineering (AWS + Python)

Build production-grade services, APIs, and workflow integrations using Python (and associated libraries/frameworks).

Design and deploy cloud-native components on AWS (serverless and/or container-based architectures as appropriate).

Apply strong engineering practices: testing, CI/CD, logging/monitoring, performance tuning, and security-by-design.

4) AI / Agentic Solutions using Claude (and related stacks)

Build production applications leveraging Claude to solve real business problems in customer environments.

Develop and deliver reusable technical artefacts that support AI workflows (e.g., agents/sub-agents, skills, integration components) used in production.

Identify repeatable deployment patterns and share learnings with internal teams to improve future delivery and scalability.

5) Enablement & Adoption ( Teach the business to fish )

Enable customer teams to use and extend solutions independently through training, playbooks, documentation, and operational runbooks.

Drive adoption by aligning stakeholders, supporting change management, and ensuring solutions fit real operating models.

Required Skills & Qualifications (Must Have)

Strong software engineering fundamentals with hands-on Python experience (APIs, microservices, automation).

Strong hands-on experience building and deploying solutions on AWS.

Experience building LLM-powered applications and workflows, including practical experience with Claude (prompting, tool-use patterns, integration into business processes).

Proven ability to translate ambiguous business problems into working solutions, and deliver iteratively with stakeholders.

Excellent communication skills; comfortable facilitating workshops with both technical and non-technical stakeholders.

Preferred Skills (Good to Have)

Experience with agentic workflow design (tool calling, orchestration patterns, guardrails, evaluation approaches).

Familiarity with structured and unstructured data pipelines and practical understanding of monitoring/governance considerations for AI solutions

Prior client-facing delivery experience in consulting, field engineering, or forward-deployed roles.