Product & Tech | Senior Forward Deployed Engineer - AI & Data

<p>Singulier is a strategy, transaction advisory and AI transformation consulting firm supporting private equity funds, portfolio companies and established businesses on their most critical strategic and operational challenges. We combine strategic thinking with hands-on execution to help our clients make better decisions, accelerate value creation and deliver tangible business impact.&nbsp;</p><p><strong>Singulier x OMMAX</strong> are joining forces to build a leading European AI management consulting platform, combining deep strategic expertise with technology, data and AI capabilities. Together, we support clients across the full value creation journey, from strategy and due diligence through transformation and execution.&nbsp;</p><p>We operate in a partner-led, entrepreneurial environment where everyone is expected to think critically, take ownership early and work directly with senior stakeholders.&nbsp;</p><p></p><p><strong>What you will do&nbsp;</strong></p><p>As a Senior Forward Deployed Engineer, you will design, build and deploy AI, data and software solutions directly with our clients. </p><p>You are an engineer first: you write production code and take technical ownership of complex projects from discovery through production and handover.</p><p>You will work with client teams, alongside Singulier engineers, Product Owners, delivery managers and advisory colleagues:</p><ul><li><strong>Build and ship:</strong> write, review and debug production code. Build applications, integrations, data pipelines and AI workflows or agents that connect models to client data, tools and processes.&nbsp;</li></ul><ul><li><strong>Design the solution:</strong> translate business priorities into architecture, technical scope and estimates, and make explicit trade-offs between quality, cost and speed.&nbsp;</li></ul><ul><li><strong>Run it in production: </strong>put in place testing, AI evaluations, access controls, monitoring and deployment suited to the client's systems and constraints.&nbsp;</li></ul><ul><li><strong>Drive adoption:</strong> understand workflows, test assumptions with users and explain technical choices to engineers and executives. Agree success measures upfront, track adoption and business impact, and hand over with documentation and training.&nbsp;</li></ul><ul><li><strong>Lead technically: </strong>guide other engineers through design and debugging, coordinate technical dependencies, and turn project work into reusable components and patterns. This is a senior individual contributor role.&nbsp;</li></ul><p></p><p><em>Beyond client work, you will help build the firm: scoping and estimating new projects, interviewing engineering candidates, and contributing to internal AI initiatives and experimentation.&nbsp;</em></p><p></p><p><strong>Work environment : engineering culture and stack&nbsp;</strong></p><p>We are building a small, highly skilled delivery team that shares engineering patterns and goes deep on a focused stack. Our aim is to make AI and technology transformation practical for mid-sized companies, within their budgets, teams and operating realities. Reusing proven foundations across projects lets us deliver faster, with consistent quality and a clear path to business value.&nbsp;</p><p><strong>The stack below is our default, not a constraint: </strong>we adapt to client environments and adopt new tools when the benefit is clear.&nbsp;</p><ul><li><strong>Ways of working: </strong>Git, pull requests, code reviews, automated tests and CI/CD. Monitoring, evaluations and tracing ship with every AI application.&nbsp;</li></ul><ul><li><strong>Languages and frameworks: </strong>Python, SQL and TypeScript; Polars, FastAPI, Pydantic and Pydantic AI.&nbsp;</li></ul><ul><li><strong>Data:</strong> PostgreSQL, DuckDB, LanceDB and Parquet, with attention to data quality, modelling and the simplest design that meets performance and scale needs.&nbsp;</li></ul><ul><li><strong>Infrastructure: </strong>infrastructure as code, Docker, and disciplined configuration, secrets and environment management. Databricks and Google Cloud, including BigQuery, when the workload calls for them.&nbsp;</li></ul><p></p><p><em>You do not need to have used every tool listed. We care about what you have shipped, how you collaborate and what production has taught you.&nbsp;</em></p><p><strong>Requirements</strong></p><p><strong>You will likely bring:&nbsp;</strong></p><ul><li>6+ years as a hands-on engineer in software, data or applied AI, including technical ownership of complex production deployments.&nbsp;</li></ul><ul><li>Strong Python, fluent SQL and enough TypeScript to contribute across the stack.&nbsp;</li></ul><ul><li>Experience shipping LLM-based solutions to production: evaluating their behaviour and designing around their limitations in real workflows.&nbsp;</li></ul><ul><li>Solid grounding in APIs, data modelling, storage and pipelines, and practical experience with cloud services and infrastructure as code.&nbsp;<