Senior AI/LLM Engineer

Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with the organization of the first Data Science UA conference, setting the foundation for our growth. Over the past 9 years, we have diligently fostered the largest Data Science Community in Eastern Europe.

About the client:

Our client is building an innovative platform that’s reimagining how personal coaching works in the digital age. The solution connects experts with their audience through AI-powered coaching programs that scale personalized guidance.

The challenge company is solving is twofold: users want personalized guidance through structured programs, but coaches have limited time and capacity to assist individuals. AI system bridges this gap by automating coaching interactions while maintaining the coach’s unique voice and expertise.

About the role:

We’re looking for a Senior/Lead AI Engineer to join the team and help build intelligent agentic systems powered by LLMs.

Requirements:

ML Expertise:

– 5+ years of overall AI/ML experience.
– Practical experience in deploying classical ML models into production environments or APIs.
– Hands-on experience with scikit-learn, XGBoost, LightGBM, or similar frameworks for structured data modeling.
– Demonstrated ability to work with structured data and extract insights through statistical and ML methods.
– Experience designing end-to-end ML workflows, from data preprocessing to model evaluation.
– Ability to integrate classical ML models into larger AI systems, contributing to overall system intelligence.

LLM expertise:

– 2+ years of experience with AI/ML systems, with significant focus on language models.
– Real experience building agentic systems, not just chatbots – the platform requires both conversational AI interfaces and sophisticated background agents that autonomously monitor progress, analyze patterns, and trigger interventions.
– Proven experience building production applications with LLMs that go beyond simple Q&A – including multistep reasoning, goal-oriented behavior, and autonomous decision-making.
– Experience with major LLM providers (OpenAI, Anthropic, Google, etc.) – with preference for Google Gemini experience given the tech stack.
– Strong expertise in prompt engineering for both interactive and autonomous agent behaviors.
– Experience with LLM function calling and structured outputs.
– Experience designing AI systems that generate dynamic UI components and structured data for frontend consumption.
– Deep understanding of AI context management, retrieval, and memory systems.
– Proficiency with structured output frameworks for controlling LLM behavior and reasoning patterns.
– Experience designing and implementing agent orchestration systems that coordinate multiple AI workflows.
– Experience with SQL databases, including both raw SQL and ORM tools like Drizzle.
– Knowledge of vector databases for AI applications and similarity search.
– Strong algorithmic background and problem-solving skills.
– Ability to stay current with the latest AI research and implement emerging best practices.

Nice to have:

– Deep experience with Google Gemini API – including advanced features like structured outputs, function calling, and multi-modal capabilities.
– Experience migrating between different LLM providers and understanding their trade-offs.
– Understanding of knowledge graphs and experience building them.
– Background in machine learning and related AI technologies.
– Advanced knowledge of TypeScript with in-depth understanding of the type system.

Responsibilities:

Classical ML:

– Design and implement classical ML models (e.g., decision trees, logistic regression) for tabular data analysis and prediction.
– Apply supervised and unsupervised ML techniques to structured datasets for segmentation and anomaly detection.
– Build data pipelines to preprocess, clean, and transform tabular user engagement and program metrics data.
– Integrate ML outputs into LLM-driven systems to enhance personalization and reasoning accuracy.
– Evaluate ML models using cross-validation, performance metrics, and feature importance analysis.

Generative AI:

– Design and implement AI systems that deliver personalized coaching through dynamic programs.
– Build autonomous agent systems that proactively monitor user engagement, detect coaching opportunities, and trigger appropriate interventions without human oversight.
– Architect AI-driven UI systems where interface elements, user flows, and content presentation are dynamically generated and adapted by AI based on user context and program requirements.
– Develop AI analytics capabilities that help program creators monitor participant progress and identify coaching opportunities.
– Create efficient data models and queries that capture coaching relationships, program structures, user progress, and interaction patterns.
– Build and optimize AI workflows that scale expertise while following structured coaching programs.
– Design agent orchestration systems that coordinate between conversational interfaces and background analytical agents.
– Collaborate with frontend teams to define how AI systems generate and control UI components dynamically.
– Implement systems for continuous AI improvement based on user engagement and program effectiveness data.
– Collaborate across engineering teams to integrate AI functionality with frontend and database components.
– Optimize AI performance and reliability in the Cloudflare-based environment.

The company offers:

– Competitive option stock & salary-based compensation.
– Remote work.
– Paid vacation and sick leaves.

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