Forward Deployed AI Engineer

gravity9 is a boutique IT consulting company headquartered in the UK with offices in the US Canada Poland and Colombia. Our team has deep experience in engineering experience design and product management. We enjoy a challenge and pride ourselves on working with our clients on their most complex problems finding elegant and flexible solutions that help them transform their businesses.

Join Our Growing Team - Future Opportunities Await!

We are excited to share that our company is experiencing significant growth and expansion and as a result were 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 youre 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.

What does the recruitment process look like

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.

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.

Role Description

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 clients 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 clients own product.

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

• Embed with the client. Work hand-in-hand inside the clients 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 dont 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.

Technical skills

Essential

• 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.

Strong advantage

• 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.

Nice to have

• 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.

Soft skills

An FDE is an engineer who is safe in front of a client. We screen as hard on this section as on the technical one.

• Client presence and credibility. Can hold a technical conversation with a client architect and a business conversation with their CTO or head of operations in the same hour and be trusted by both. Can present whiteboard and answer hard questions without deflecting.

• Translation. Turns a business problem described by non-technical people such as a clinician campaign manager or supply-chain planner into a technical design and explains the technical design back in their language.

• Ego-free collaboration in a supporting role. On some engagements the tech lead will come from the client or a partner not from us. You need to contribute strongly disagree well and take direction without friction. Brilliant-but-territorial doesnt work here.

• Comfort with ambiguity and greenfield. Engagements often start before the requirements the data access or sometimes the problem statement are settled. You make progress anyway and you make the ambiguity visible rather than hiding it.

• Bias to production. Instinctively asks how does this get deployed monitored and maintained rather than stopping at a working notebook.

• Teaching instinct. Actively enjoys upskilling the clients engineers because self-sufficiency at handover is the definition of success not follow-on billing.

• Ownership and pace. Short engagements small teams no layers to hide behind. You unblock yourself chase the access request and follow the thread to the answer.

• Commercial awareness. Understands that scope cost and the clients willingness to pay are part of the engineering problem and contributes to scoping and estimating honestly.

• Resilience and adaptability. New client new domain new stack every few months; occasional travel; occasionally a sceptical stakeholder who has been told AI is coming for their job. You stay steady and constructive.

• Written communication. Clear design docs decision records handover material and status updates in English. Much of this is asynchronous and cross-timezone.

• Genuine curiosity about the field. This ecosystem changes monthly. We want people who are already reading building side projects and forming opinions not waiting for training to be scheduled for them.

Experience profile

• 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 37 people can act as engagement tech lead contributes to pre-sales and estimation and can mentor a growing FDE bench.

What success looks like

• 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.

What we offer

• Support for vendor certifications and access to partner training programmes.

• Company-wide AI tooling as part of how we work day to day.