Senior AI Engineer

Established in 1997, Softeq was built from the ground up to specialize in new product development and R&D, tackling the most difficult problems in the tech sphere. Now we've expanded to offer early-stage innovation and ideation plus digital transformation business consulting. Our superpower is to deliver all of this under one roof on a global scale. So let's get started and build a better future together! We're looking for a Senior AI Engineer to join our team and contribute to its continued growth. As we're expecting to expand our team and launch new projects within the next 1–2 months, we're already accepting applications and starting the interview process for selected candidates. We'd love to hear from you - feel free to apply! What the role does here: - Builds and hardens an AI assistant grounded on a curated data layer; - Delivers the product AI features: personalized recommendations, generated summaries and briefings, in-product assistance, learning suggestions, and detection of irregularities in reporting; - Owns prompt and context orchestration, retrieving the right data from the catalog while respecting the requesting user permissions; - Builds and maintains indexes for semantic search and keeps them current as the underlying tables change; - Implements guardrails: resistance to instruction injection through user input and through retrieved data, hard limits on accessible scope, output filtering; - Builds evaluation practice: example sets, regression runs when a model or prompt changes, and acceptance criteria; - Monitors inputs, outputs, quality, latency and cost per request, and supports explainability of answers; - Ships AI features into the service layer and the application interface. Requirements: - Python plus real production experience integrating large language models; - Retrieval-augmented generation in practice: context assembly, relevance evaluation, working within context limits, vector indexes and keeping them fresh; - Databricks as a context source: reading from the catalog, honouring grants and row-level rules when placing data into a prompt, platform vector search, and invoking models from SQL and jobs; - Prompt and context engineering as a versioned, repeatable process; - Evaluation of AI features: test sets, metrics, regression when the model updates; - AI security: prompt injection, cross-tenant leakage, output filtering; - FastAPI for the service side, and enough React to land a feature in the interface. Nice to have: - Publishing and monitoring models as endpoints on the platform, with a single access point that handles limits and cost; - Azure OpenAI or another managed model service; - Assistants in multi-tenant environments with hard data isolation; - LLM observability: tracing, cost accounting, investigating answer-quality incidents; - Shipping AI features to external users rather than internal staff only. Softeq communicates only from @softeq.com email addresses. We never request payments or fees for any reason during hiring — including trainings or courses to be completed, equipment, onboarding, or background checks — and we will not ask for banking information, cryptocurrency or gift cards. If you receive a message from any other domain or requesting payment, do not respond and report it to abuse@softeq.com