Forward Deployed AI Engineer

We are excited to share that our company is experiencing significant growth and expansion, and as a result, we're on the lookout for talented individuals to join our team.

We want to be upfront about our hiring process. At this moment, we are in the process of securing projects that align with our business goals and objectives. Our intention is to ensure that once we bring new team members on board, they will have a meaningful and impactful role to play from day one. While we might not be able to extend an offer immediately, we are highly interested in considering you for these future projects.

If you're open to the idea of potentially joining our team in the future, we encourage you to continue with our interview process. We value your time and effort and believe that getting to know you better will help us make an informed decision.

Once you have effectively concluded the entire recruitment procedure, we will retain your profile and reach out to you once a suitable opportunity emerges. Additional interviews will not be necessary at this stage, and we anticipate being able to extend an offer to you promptly.

1. Recruiter Screen

In the first call, our recruiter will learn more about you and your story to check a potential fit for gravity9. This is also an opportunity to ask your questions about the role and company. This step might take around 60 mins.

2. Tech 1 Interview with our AI Practice Lead

In this meeting your potential future teammate will take a deeper dive into your experience and what you could bring to the team. This step will take around 75 mins.

3. Tech 2 Interview with our software consultants

In this meeting your potential future teammate will take a deeper dive into your experience and what you could bring to the team. This step will take around 60 mins.

4. Final Interview

You made it to the very last stage! Here we already strive to cooperate with you and give you an opportunity to meet our leadership team during this informal talk. This step will take around 30 mins.

The salary details for the role will be communicated ahead of the first recruitment meeting.

Thank you for considering the opportunity to be a part of our growing journey. We look forward to the possibility of working together and achieving great things.

gravity9 is expanding its Forward Deployed Engineering team to build production agentic AI systems for enterprise clients, in partnership with leading frontier-model providers. We already support clients from different verticals to have agentic and RAG systems live in production across healthcare, financial services, retail and global logistics.

As a Forward Deployed AI Engineer you work embedded in the client's environment, from discovery, through architecture and build, to production and handover. This is end-to-end agent engineering, not proofs of concept and not advisory work. Two flavours of engagement:

Internal enterprise use cases, such as reconciliation and KYC workflows in financial services, clinical and claims workflows in healthcare, operations and supply-chain workflows in logistics.

Product- and customer-facing agents, more greenfield, often exploratory, built into the client's own product.

Engagements are typically a team of engineers over a few months, working shoulder-to-shoulder with the client's team including architects, DevOps and QA.

Embed with the client. Work hand-in-hand inside the client's environment, codebase and cloud tenancy, often in a hybrid team alongside their engineers. You are visible to the client from day one.

Design and build production agentic AI systems. Multi-agent orchestration, tool and function calling, retrieval, planning and routing, human-in-the-loop checkpoints, state and checkpointing, guardrails, failure handling and recovery.

Do the unglamorous data work. A large share of every engagement is data engineering: ingestion, flattening deeply nested structures, extracting content from unstructured documents, classification, summarisation, tagging, enrichment, indexing. Models reason over data, bad data beats a good model every time.

Own evaluation and accuracy. Establish a baseline eval dataset at the start of the engagement, automate grading, and track groundedness, faithfulness and retrieval quality per tool, not just at the agent level. Be ready to defend accuracy numbers to a sceptical enterprise stakeholder.

Engineer for cost and latency. Model selection and routing (cheaper, faster models for non-reasoning steps; frontier models where reasoning genuinely earns it), prompt and context budgeting, caching. Cost is a non-negotiable metric on every engagement.

Ship it properly. Observability and tracing, CI/CD, IaC, monitoring the client can actually operate, security and compliance review.

Transfer knowledge deliberately. We don't run a long-term support business. Every engagement is designed so the client owns and can extend the system after we leave. You architect with their team in the room, pair with their engineers, and hand over working monitoring and documentation.

Programming: Strong Python (async, typing, testing, packaging). Working competence in TypeScript / Node is a plus.

LLM application engineering: Hands-on production experience with frontier models: prompt and context engineering, structured outputs, tool and function calling, streaming, token and context-window management.

Agentic architecture: Built and shipped multi-agent or agentic workflows, planner/router patterns, supervisor and sub-agent designs, ReAct-style loops, state and checkpointing, retries and interrupts, human-in-the-loop review gates.

Frameworks: At least one of: Anthropic Agent SDK, LangGraph, LlamaIndex, or an equivalent orchestration framework, plus the judgement to know when to use none of them.

RAG and retrieval: Chunking strategy, embeddings, hybrid and semantic search, re-ranking, citation and provenance, natural-language-to-query translation.

Data platforms: NoSQL databases such as MongoDB (aggregation pipelines, Atlas Search, Atlas Vector Search) or a strong equivalent, plus SQL. Comfortable designing schemas for agent retrieval, not just for OLTP.

Data engineering: Building ingestion and enrichment pipelines over messy structured and unstructured sources, documents, PDFs, object storage.

Evaluation: Building eval harnesses and eval datasets, LLM-as-judge with its limits understood, regression testing of prompts and agents, metric selection per use case.

Cloud: Production delivery on AWS (incl. Bedrock), Azure or GCP, containers, serverless, networking basics, secrets management, IAM.

Engineering discipline: Git, code review, testing, CI/CD, IaC (Terraform or equivalent), observability.

Anthropic certification (Claude Developer / Anthropic-issued credential), an explicit advantage at shortlisting.

Anthropic Agent SDK production experience, a significant bonus.

MCP (Model Context Protocol): building servers and clients, tool exposure, auth patterns.

Claude Code as an autonomous SDLC agent: sub-agents, hooks, custom skills, agent marketplaces, context sharing across a team.

LLM observability and tracing tooling (LangFuse, LangSmith, Arize, Braintrust or similar).

Regulated-environment delivery: HIPAA, GDPR, SOC 2, FCA/PRA, PII handling, data residency, guardrails and red-teaming.

Graph or taxonomy-based knowledge representation alongside vector retrieval.

Kafka / streaming, Databricks, or comparable large-scale data platform experience.

Voice and multimodal agents; evaluation of non-text outputs.

FinOps for AI workloads; unit-economics modelling for agent systems.

Open-source contribution to the agent / LLM ecosystem, or conference speaking.

Prior experience as an FDE, solutions architect, or delivery consultant at a frontier-model, data platform, or infrastructure vendor.

FDE (mid): 3+ years software engineering, 1+ years hands-on LLM / agentic work with at least one system live in production. Client-facing exposure.

Senior FDE: 5+ years engineering, 2+ years AI or agentic AI, has owned the architecture of at least one production agent system end-to-end and led a client conversation about it.

Lead FDE: 7+ years, has led delivery teams of 3–7 people, can act as engagement tech lead, contributes to pre-sales and estimation, and can mentor a growing FDE bench.

First 90 days — owning a meaningful component of a client-facing agent system, including its evals, and trusted in front of the client.

6 months — designing agent architectures independently, leading a workstream, and running the handover and knowledge-transfer track.

12 months — a reference point for others on the team, contributing to reusable assets, pre-sales and interviewing.