Forward Deployed Graduate Engineer

Coforge; Greater London; On-site; GBP 40,000 - 60,000; Full time

Job summary

Coforge is seeking a founding cohort of Forward Deployed Engineers in London to turn AI pilots into production systems that move business metrics. You'll join a 60–90 day intensive program focusing on applied AI engineering, LLMs, RAG, agentic workflows, and client-mirrored capstones.

From day one you’ll work with senior FDEs on real projects, embed with Fortune 500 clients, ship production-grade code, and present updates to stakeholders up to C-suite.

Qualifications

• BS/MS in Computer Science, Engineering, Math, Statistics, Physics, or similar STEM field (or equivalent proven self-taught expertise with public portfolio).
• Strong Python skills with ability to read modern programming languages.
• Experience building applications using LLM APIs (OpenAI, Anthropic, Google) with understanding of RAG, embeddings, vector retrieval, and evaluations.
• Daily user of AI coding tools (e.g., Cursor, Copilot, Claude Code).
• Familiarity with Git, CI/CD basics, Docker, and working in collaborative codebases.
• Ability to write concise executive-level one-page documents and present to non-technical audiences.

Responsibilities

• Translate ambiguous client problems into deployed AI solutions without predefined specifications.
• Progress to owning workstreams independently (~9-12 months) and leading deployments (~18-24 months).
• Mentor incoming associates and support their onboarding.

Skills

Python

LLM APIs

AI tooling

Git

Docker

CI/CD

Executive documents

Education

BS/MS in CS or related STEM

Tools

LangGraph

CrewAI

AutoGen

MCP

OpenAI APIs

Job description

Key Skills - StrongPython, LLM APIs (OpenAI, Anthropic, Google), AI coding tools (e.g., Cursor, Copilot, Claude Code).

Experience - 0 - 2 years

Location – London, UK

About the Team

The Coforge FDE Practice is the tip of the spear for our enterprise AI work. Our Forward Deployed Engineers embed with Fortune 500 clients across Financial Services, Insurance, Healthcare, Travel, and Retail to take AI from an "interesting pilot" to "production system that moves a business metric." We're scaling the practice over the next year, and we're hiring a founding cohort of FDEs to build that scale-up alongside us.

About the Role

• Join a 60–90-day intensive program to build a foundation in applied AI engineering (LLMs, RAG, agentic systems, evaluations), vertical immersion, and a client-mirrored capstone.
• Work alongside Senior FDEs from day one, contributing to real projects instead of classroom-style learning.
• Embed with enterprise clients (typically Fortune 500) to deliver production AI systems in 2–4-week sprints.
• Co-own workstreams covering agentic workflows, RAG pipelines, eval frameworks, fine-tuning, or AI-led transformation of legacy systems.
• Build and ship production-grade code from day one for real users.
• Present weekly updates to stakeholders (engineering to C-suite), including readouts, demos, and design defense.
• Translate ambiguous client problems into deployed AI solutions without predefined specifications.
• Progress to owning workstreams independently (~9-12 months) and leading deployments (~18-24 months).
• Mentor incoming associates and support their onboarding.
• BS/MS in Computer Science, Engineering, Math, Statistics, Physics, or similar STEM field (or equivalent proven self-taught expertise with public portfolio).
• Strong Python skills with ability to read modern programming languages.
• Experience building applications using LLM APIs (OpenAI, Anthropic, Google) with understanding of RAG, embeddings, vector retrieval, and evaluations.
• Daily user of AI coding tools (e.g., Cursor, Copilot, Claude Code).
• Familiarity with Git, CI/CD basics, Docker, and working in collaborative codebases.
• Ability to write concise executive-level one-page documents.
• Ability to present technical concepts clearly to non-technical audiences.
• Experience with agentic frameworks (LangGraph, CrewAI, AutoGen, Mastra, OpenAI Agents SDK, MCP).
• Exposure to fine-tuning methods (LoRA, full fine-tuning, RLHF basics).
• Familiarity with evaluation and observability tools (LangSmith, Braintrust, Arize, Weights & Biases).
• Hands-on experience or certification in AWS, Azure, or GCP.