> Markdown version of [/jobs/ext/2797461-senior-ai-engineer-forward-deployed-engineer](https://www.wearedevelopers.com/jobs/ext/2797461-senior-ai-engineer-forward-deployed-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI Engineer / Forward Deployed Engineer - **Company:** Towards AI, Inc. - **Location:** Greater London, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Computing, Databases, Content Analysis, Continuous Integration, Data Governance, Cursor (Graphical User Interface Elements), Software Debugging, Python (Programming Language), Knowledge Management, PostgreSQL, Metadata, Regression Testing, Next.js, Software Engineering, Systems Integration, TypeScript, AI Infrastructure, Data Logging, ReactJS, Large Language Models, Git, Code Restructuring, Docker - **Published:** September 8, 2026 - **Apply:** https://www.collegerecruiter.com/job/2840621430-senior-ai-engineer--forward-deployed-engineer ## About the Role You should also actively design reusable AI infrastructure for the firm: Claude Skills, Model Context Protocol (MCP) servers, sub-agents, eval harnesses, prompt patterns, and workflow templates. Our strongest engineers build the tooling that makes the rest of the team faster. * - 5+ years professional software engineering experience. * - 2+ years building LLM applications, RAG systems, AI agents, or AI workflow automation in production. * - Shipped at least two AI systems beyond simple chat: domain-specific RAG, multi-tool agents, document analysis, internal copilots, or comparable. * - Strong Python, plus production experience with application programming interfaces (APIs), integrations, and data-heavy systems. * - Hands-on with OpenAI, Anthropic, Gemini, Bedrock, Vertex AI, or comparable LLM APIs. * - Real RAG depth: embeddings, chunking, indexing, metadata, hybrid search, reranking, retrieval evaluation, grounding. * - Real agent depth: tool calling, structured outputs, orchestration, state, retries, safe execution boundaries. * - Strong eval discipline: golden sets, regression tests, human review loops, considered use of LLM-as-judge, release gates, production monitoring. * - Production judgment on latency, cost, scalability, reliability, privacy, security, and data governance. * - Comfort with Docker, Git, CI/CD, cloud deployment, databases, logging, monitoring. * - Daily, expert use of agentic coding tools (Claude Code, Codex, Cursor) for real engineering, with careful inspection of what they produce. * - Strong technical communication. You can explain architecture, trade-offs, and risk to engineering counterparts and to your own pod, and hold your own in a senior client conversation when the strategist hands you the floor. * - Prior work with investors, PE firms, venture funds, family offices, or portfolio companies. * - AI experience in investment research, due diligence, finance, sales, customer support, reporting, or knowledge management. * - TypeScript, React, Next.js, Postgres, pgvector, Pinecone, Weaviate, or Qdrant. * - LangChain, LangGraph, LlamaIndex, LangSmith, Langfuse, Braintrust, OpenTelemetry, MCP servers, OpenAI Agents SDK, or Claude Skills. * - Solutions engineering, customer engineering, founder-led product, or enterprise implementation backgrounds. * - Fine-tuning, distillation, or open-weight model experience. A builder and a field engineer. You take an ambiguous technical problem, identify the constraint that matters, and design a system that gets used rather than admired. You move between architecture and code without losing either. You know when an agent is the right shape, when deterministic code is better, when retrieval is the bottleneck, and when a proposed scope is the wrong one. You are AI-native enough that work without these tools feels broken. You should be able to describe a system you have built in concrete terms: problem, architecture, model choices, retrieval design, evals, failure modes, cost profile, rollout plan, and what you would change next time. ## Description We architected the Agentic Intelligence Engine for Nviya-Prime, a B2B FinTech replicating institutional-grade banker reasoning. Three coordinated sub-agent services orchestrate ~800 prompts per client analysis. Event Targeting classifies material signals across economic, geopolitical, regulatory, market, supply-chain, and idiosyncratic domains. Predictive Scanning runs 24/7, scoring live events on materiality x urgency and surfacing prioritised next-best-actions, for example an FX hedge plus interest-rate contract in response to a funding-cost spike. Production stack: agentic retrieval-augmented generation (RAG), hybrid search, contextual retrieval, reranking, model routing, LLM-as-judge evaluation, end-to-end observability. Currently shipping into live demos with global banks across Europe. Two modes, depending on the project. For most engagements you work from our team alongside your engineering pod, with regular technical touchpoints into the client's engineering and data teams. A deployment strategist on each engagement owns the executive client relationship and the bulk of stakeholder management, leaving you to focus on getting the system built right. For our largest multi-quarter engagements, typically with financial services firms, PE houses, or strategic portfolio companies, you go forward-deployed at the client site for kickoff sprints, key build phases, and production rollout. Your direct client counterparts are engineering, data, product, and end-user teams. You should be excited by this mode of working: it offers the fastest feedback loops and the most direct influence on what gets shipped of any client setup we run. * - Lead technical scoping and architecture across client engagements. * - Build production LLM applications using OpenAI, Anthropic, Gemini, Bedrock, Vertex AI, or similar. * - Architect RAG systems: retrieval quality, grounding, source attribution, metadata, hybrid search, reranking, access control. * - Build agentic workflows: tool use, structured outputs, orchestration, retries, fallbacks, state, human review. * - Design eval frameworks for prompts, retrieval, agents, models, regressions, safety, latency, and cost. * - Define guardrails for privacy, governance, prompt injection, sensitive tool use, and hallucination risk. * - Deploy and monitor with Docker, cloud infrastructure, continuous integration and deployment (CI/CD), logging, and observability. * - Pair with client engineering and data teams on integrations, deployment, and technical handover. * - Mentor junior engineers; hold the technical quality bar across delivery. * - Ship reusable firm assets: reference architectures, prompt libraries, eval harnesses, agent skills, implementation playbooks. Daily, expert use of Claude Code, Codex, Cursor, or similar agentic tools is non-negotiable. Planning, codebase exploration, implementation, testing, refactoring, documentation, research, debugging: all of it. Engineers who already work this way outpace traditional engineers by a wide margin, and that gap is the bar for this role. The standard is intelligent supervision, not blind delegation. You give the agent the right context, constrain the task, inspect the diff, run the tests, catch weak assumptions, and decide when to take the keyboard back. You ship at speed but never ship what you have not understood. ## Related Videos - [Watch Tests Go Brrrr! : Getting Started with Cypress in ReactJS](https://www.wearedevelopers.com/videos/282-watch-tests-go-brrrr-getting-started-with-cypress-in-reactjs) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Agentic employees in world's most downloaded FinTech app](https://www.wearedevelopers.com/videos/100123-agentic-employees-in-world-s-most-downloaded-fintech-app) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [Engineering Mindset in the Age of AI - Gunnar Grosch, AWS](https://www.wearedevelopers.com/videos/1735-engineering-mindset-in-the-age-of-ai-gunnar-grosch-aws) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Never delegate the understanding](https://www.wearedevelopers.com/magazine/749-never-delegate-the-understanding) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care)