AI Implementation Engineer
Models and demos are the easy part. Making AI deliver measurable value inside a customer's existing data, workflows and organisational constraints is where most implementations stall — and this role exists to solve exactly that.
As an AI Implementation Engineer you sit at the intersection of engineering and delivery. You embed with client teams, understand the business problem they are actually trying to solve, then build the RAG pipelines, LLM integrations, data feeds, guardrails and observability that turn a promising prototype into a dependable production system. The work is hands-on throughout: you write the code, own the architecture decisions at the client layer, and stay accountable for the outcome.
Clients will rely on you as their technical AI advisor — which means communicating clearly with non-engineers, scoping work honestly, and feeding real-world learnings back into the broader delivery playbook. The role spans a wide range of environments, including regulated industries, so comfort with ambiguity, fast iteration and rigorous standards all matter in equal measure.
Who thrives here
Engineers who are energised by ownership across the full stack — from raw data to live inference — and who find the constraints of real enterprise environments more interesting than frustrating. You prefer concrete outcomes over polished slides, and you are comfortable being the person in the room who makes it work.
What you'll own
- Embed with client teams to map their data, workflows and the specific business problem AI needs to address
- Build, integrate and deploy GenAI/LLM solutions in client environments — covering RAG pipelines, LLM/API integrations, agents, prompt design and guardrails
- Design and build the data pipelines and APIs that feed and surround the AI system, ensuring reliability, safety and observability
- Evaluate, monitor and operationalise AI in production — tracking accuracy, latency, cost and safety
- Act as the client's primary technical AI advisor throughout the engagement
- Capture real-world implementation learnings and feed them back into the delivery playbook and product direction
What it takes
- 3–8+ years of software engineering experience with demonstrable hands-on GenAI/AI implementation work; strong Python
- Practical LLM/GenAI experience: LLM APIs (OpenAI, Anthropic or open-source models), RAG, vector databases, LangChain or LlamaIndex, prompt engineering
- Strong integration, API and data-pipeline skills; at least one major cloud platform (AWS, Azure or GCP), ideally including its AI stack (SageMaker, Azure AI or Vertex AI)
- Customer-facing ability — comfortable scoping, explaining and delivering directly with clients across technical and business stakeholders
- Able to work effectively in ambiguous, fast-moving environments with iterative delivery cycles
Nice to have
- MLOps, model fine-tuning, AI evaluation, red-teaming or AI-security exposure
- Solutions consulting or startup background; enterprise or BFSI domain knowledge
- Experience shipping AI to production at scale or deploying in regulated environments
We respond to every application within 2 business days.