AI Full - Stack Engineer / Data Scientist

This role involves working deeply with multimodal foundation models, advanced agentic workflows (including Agent-to-Agent/A2A communication), Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG) pipelines, other emerging AI technologies, and MLOps subsystems. You will utilise cloud services (AWS and multicloud) alongside vector, graph, and traditional databases to develop scalable and robust AI solutions.

The candidate will have responsibilities across the following functions:

Agentic and GenAI Application Development:

• Design and build advanced AI agentic systems, state machines, and search-based conversational systems that solve complex business problems.
• Develop workflows leveraging Large Foundational Multimodal Models to process and reason across text, audio, and video modalities.
• Implement Model Context Protocol (MCP) servers/clients to standardise context exchange between agents, data sources, and external tools.
• Collaborate with AI Architects, Product Owners, and fellow developers to integrate AI capabilities into scalable, fair, and ethical end-user applications focusing on relevance and real-time performance.

Full-Stack Engineering and Agentic SDLC:

• Leverage AI coding agents (e. g., Claude Code) daily to accelerate full-stack development cycles, maintaining high productivity across frontend, backend, and infrastructure tasks.
• Take end-to-end accountability for features: write high-quality, production-ready Python (and occasionally TypeScript) code with comprehensive testing and documentation.
• Manage the DevOps/MLOps lifecycle: containerise applications using Docker, configure CI/CD pipelines, and architect high-throughput, reliable cloud-native solutions on AWS/multicloud.

Data Science, EDA and Strategy:

• Perform thorough Exploratory Data Analysis (EDA) to understand dataset characteristics, uncover patterns, detect biases, and identify data quality issues.
• Use statistical and visualisation techniques to inform feature engineering, model selection, and optimisation of foundation model-based applications.
• Design robust data pipelines to curate, preprocess, and structure diverse datasets that maximise LLM effectiveness and reduce bias.

Algorithm Development and Optimisation:

• Design, customise, optimise, and fine-tune LLM-based and traditional AI algorithms for specific use cases (e. g., text generation, summarisation, AI agents, sequence modelling).
• Lead advanced prompt engineering strategies, utilising zero-shot, few-shot and other paradigms to optimise model outputs without extensive fine-tuning.
• Implement pre-generative AI models (e. g., classification, clustering, regression) when they provide a more efficient, interpretable, or cost-effective solution compared to LLMs.
• Optimise model inference speed, reduce latency (cold start reduction, caching strategies), and manage resource usage across cloud architectures.

Evaluation, Observability and Continuous Improvement:

• Conduct rigorous experimentation (A/B testing) and implement automatic metric pipelines (e. g., BLEU/ROUGE, RAG retrieval accuracy, human rating frameworks, etc. ) to evaluate generative and multimodal systems.
• Implement real-time algorithms monitoring and observability practices, ensuring visibility into pipeline behaviour, drift detection, and anomaly identification using telemetry tools.
• Translate complex technical results into clear, actionable insights for stakeholders, driving data-driven decision-making.

Requirements:

• 7+ years of experience in AI/ML engineering and Data Science, with exposure to generative AI, agents and classical ML.
• 3+ years of hands-on experience with generative large language models and agents.
• Proven experience managing the end-to-end MLOps lifecycle and deploying models at scale.
• Proven experience leveraging AI coding agents within an agentic SDLC to accelerate feature delivery.
• Hold a B. Sc., B. Eng., M. Sc., M. Eng., PhD, or equivalent in Computer Science, Physics, Statistics, Mathematics, or a related field.
• Deeply passionate about AI, staying continuously up-to-date with the latest developments in foundational models, agentic approaches, and classical ML.
• Team-oriented, proactive, and collaborative, thriving in a fast-paced environment where roles are fluid and multidisciplinary.
• Great communicator, able to present complex findings clearly to both technical and non-technical audiences.
• Able to communicate in English at the level of C1+.
• Ready to span their work times across the Central European Timezone on occasion, when necessary.

Technical Skills:

• Programming: Advanced production-grade proficiency in Python. Additional proficiency in TypeScript is considered an advantage.
• AI/GenAI Frameworks: Deep expertise with LangChain or similar LLM orchestration frameworks, agentic system design, and state machine logic.
• Cloud Platforms: Practical experience designing cloud solutions on AWS (familiarity with multicloud is a plus), utilising GenAI-specific services (e. g., Amazon Bedrock, SageMaker).
• Databases: Strong knowledge of vector databases (e. g., AWS OpenSearch), graph databases, Snowflake, SQL, NoSQL, and data lakes.
• Software Engineering Best Practices: Expertise in API design, CI/CD, Docker, Infrastructure as Code, and rigorous automated testing.
• Data Skills: Strong background in statistics, data manipulation, synthetic data generation, and feature engineering.

Capabilities:

• Problem-Solving Skills: Excellent analytical skills to tackle complex engineering and statistical challenges.
• Ownership and Leadership: Deep sense of accountability, eager to define architectural patterns, and able to step into a Tech Lead role when necessary.
• Consulting: Ability to work closely with stakeholders across the enterprise to consult on the technological approaches to their business problems.
• Ethics: Strong understanding of biases, fairness, hallucination mitigation, and responsible AI deployment.