AI Engineer - Generative AI &Agentic Systems

Responsibilities:

• Design, develop, and implement agentic AI workflows and intelligent customer advisory capabilities to support customers throughout the end-to-end digital sales journey.
• Build and optimize LLM-powered applications, including Retrieval-Augmented Generation (RAG) solutions and multi-agent architectures.
• Develop scalable AI solutions using LangChain, LangGraph, and LangFuse.Translate complex business requirements and decision rules into reliable, scalable, and maintainable AI agent behavior.
• Design and implement prompt engineering, tool/function calling, context management, memory, and structured output strategies.
• Develop and integrate production-grade REST APIs using Python and FastAPI.Design and optimize retrieval solutions, including embeddings, vector search, ranking, retrieval optimization, and hybrid search.
• Establish quality assurance and automated evaluation approaches for AI-driven customer interactions using tools and frameworks such as DeepEval and G-Eval.
• Implement AI observability and monitoring to continuously assess application quality, reliability, and performance.
• Contribute to the deployment, operationalization, and production readiness of Generative AI solutions, including CI/CD and cloud-native environments.
• Apply software engineering best practices, including clean code, design patterns, automated testing, and maintainable architecture.
• Collaborate closely with business stakeholders, Product Owners, developers, and other technical teams throughout design, development, testing, and production rollout.
• Contribute to a cross-functional product team delivering an AI-powered Product Advisor that provides customers with personalized product recommendations and supports them from product discovery and consultation through contract completion.

Requirements:

• Strong professional experience in Python development.
• Hands-on experience designing and developing production-grade REST APIs with FastAPI.
• Solid understanding of software engineering principles, including clean code, design patterns, automated testing, and scalable application architecture.P
• roven practical experience developing and deploying Generative AI and LLM-powered applications.
• Strong knowledge and hands-on experience with:
• Prompt Engineering
• Function Calling / Tool Calling
• Agentic AI systems
• Multi-Agent architectures
• Context Management
• Memory Systems
• Structured Outputs

Hands-on experience designing and implementing Retrieval-Augmented Generation (RAG) solutions.

• Strong understanding of embeddings, vector databases, ranking, retrieval optimization, and hybrid search.
• Experience with Azure AI Search or comparable search technologies.
• Practical experience with relevant GenAI frameworks and tools, particularly:
• LangChain
• LangGraph
• LangFuse
• DeepEval / G-Eval
• OpenAI APIs
• AI observability and monitoring solutions

Experience working with data and infrastructure technologies such as:

• PgVector or other vector databases
• Redis
• Docker
• Kubernetes
• CI/CD pipelines

Experience working with cloud platforms, preferably Microsoft Azure.

• Ability to translate complex business requirements into robust technical solutions.
• Strong communication and collaboration skills with the ability to work effectively in cross-functional product teams.