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.