Full - Stack AI Engineer (Data Science)

The core responsibilities for the job include the following:

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 standardize 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: containerize 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 visualization techniques to inform feature engineering, model selection, and optimization of foundation model-based applications.
• Design robust data pipelines to curate, preprocess, and structure diverse datasets that maximize LLM effectiveness and reduce bias.

Algorithm Development and Optimization:

• Design, customize, optimize, and fine-tune LLM-based and traditional AI algorithms for specific use cases (e. g., text generation, summarization, AI agents, and sequence modeling).
• Lead advanced prompt engineering strategies, utilizing zero-shot, few-shot, and other paradigms to optimize 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.
• Optimize 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 for monitoring and observability practices, ensuring visibility into pipelines' behavior, 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., Ph. D., or equivalent in computer science, physics, statistics, mathematics, or a related field.
• Be deeply passionate about AI, staying continuously up-to-date with the latest developments in foundational models, agentic approaches, and classical ML.
• Be team-oriented, proactive, and collaborative, thriving in a fast-paced environment where roles are fluid and multidisciplinary.
• Be a great communicator, able to present complex findings clearly to both technical and non-technical audiences.
• Be able to communicate in English at the level of C1+.
• Be ready to span their work times across the Central European Time Zone 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), utilizing 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.