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