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.
  

• 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