Data Scientist / AI Solution Engineer

Responsibilities: Continue developing and enhancing the existing recipe chatbot and support additional use cases, such as FAQs and other conversational AI applications. Develop and maintain AI-powered applications, agentic workflows, and API integrations. Evaluate different LLMs and select suitable models based on business requirements, performance, and latency. Define and assess appropriate evaluation metrics to measure AI solution quality. Perform basic data analysis and quality checks, and develop dashboards where required. Work with the Solution Architect (SA), Product Owner (PO), and business stakeholders to understand and clarify requirements before implementation. Scope Clarification The role primarily focuses on AI engineering, Generative AI, agentic workflows, AI application development, and Azure integration. Advanced traditional Data Science, machine learning model development, and extensive statistical modelling are not primary requirements. Data analytics related capabilities are mainly required for data-quality assessment, analytical validation, and evaluating AI models. Overall solution architecture and high-level system design are handled by designated architects. The candidate is expected to focus on implementing and integrating AI solutions within the agreed architecture RAG, LLMs & Machine Learning Design and implement Retrieval-Augmented Generation (RAG) solutions. Optimize chunking strategies, embeddings, hybrid retrieval, reranking, and retrieval quality. Evaluate and improve AI system performance using quantitative metrics. Apply machine learning techniques to predictive and prescriptive business problems. Demonstrate strong understanding of LLM architecture, tokenization, embeddings, transformers, attention mechanisms, and model limitations. Data & Business Translation Translate business requirements into measurable technical solutions and acceptance criteria. Design and optimize data models for SQL and NoSQL databases. Work with Cosmos DB and other scalable data platforms. Collaborate with product, business, and engineering teams to deliver end-to-end solutions. Profile Description: 8+ years of software engineering experience. 2 years of hands-on experience in developing agentic applications and workflows. Generative AI and Agentic AI: Hands-on experience building LLM-powered applications and agentic workflows using frameworks such as LangGraph and LangChain. AI Application Development and Integration: Ability to develop AI-powered applications, implement application logic, integrate LLMs and external services through APIs, and build end-to-end AI solutions. Azure: Practical experience with Azure services for developing, deploying, and integrating AI applications. LLM Evaluation: Ability to evaluate and compare LLMs based on response quality, latency, and suitability for specific business use cases. Data Analysis and Quality: Ability to analyse input data, identify data-quality issues, and validate whether the available data is suitable for the intended solution. Candidates should demonstrate practical implementation experience rather than theoretical knowledge or training-based exposure. Strong Python expertise with proven experience in building scalable, maintainable, and well-tested applications. Hands-on experience developing APIs using FastAPI, Pydantic, asynchronous programming, and streaming responses. Proven track record delivering production-grade AI solutions, including deployment, monitoring, troubleshooting, and operational support. Experience building Agentic AI applications using tool/function calling, structured outputs, workflow orchestration, memory, and state management. Strong understanding of RAG architectures, embeddings, hybrid search, reranking, and retrieval evaluation. Solid foundation in machine learning, statistics, experimentation, and AI model evaluation. Deep understanding of LLM concepts, including tokenization, embeddings, transformers, attention mechanisms, and model limitations. Experience implementing AI safety, observability, prompt-injection protection, and data governance practices. Hands-on experience with LangGraph or similar orchestration frameworks. Strong knowledge of SQL and NoSQL databases, including Cosmos DB and data modeling best practices. Experience working with Azure AI Foundry, Azure AI Search, Azure OpenAI, Azure Functions, Blob Storage, and related Azure services. Ability to translate business requirements into measurable outcomes and scalable technical solutions. Familiarity with CI/CD, Agile/Scrum methodologies, and modern software engineering practices. Strong problem-solving, communication, stakeholder management, and collaboration skills. Demonstrates ownership, continuous learning, knowledge sharing, and a passion for engineering excellence. Exposure to Polars, Kedro, Grafana, advanced data engineering, and advanced machine learning techniques is a plus. Nice to have Competencies: Working experience in a SAFe environment is a plus. Experience working with European clients