Our Customer:
Our customer is driving enterprise innovation through Artificial Intelligence, cloud technologies, and modern engineering practices to solve complex business challenges. The team develops scalable AI-powered solutions that accelerate digital transformation while ensuring enterprise-grade security, governance, and operational excellence.
Your Tasks:
Analyze ambiguous business challenges and translate them into AI-native solutions with clear business value and measurable outcomes.
Collaborate with business stakeholders across different functions to understand requirements, identify AI opportunities, and shape practical solutions.
Design and develop AI solutions from proof-of-concepts and MVPs to scalable, production-ready implementations.
Design and implement RAG pipelines, AI copilots, intelligent assistants, and agentic AI workflows.
Develop production-grade AI applications using Microsoft Azure, AI Foundry, Databricks, and Azure OpenAI.
Develop and configure AI agents and copilots using Copilot Studio, including topics, actions, and integrations with enterprise data sources.
Design and implement agentic and multi-agent architectures, including planner/executor and supervisor patterns, tool use, and autonomous workflows.
Establish AI guardrails, evaluation frameworks, validation processes, and responsible AI practices.
Define and implement approaches for AI-output quality testing, offline and online evaluation, and continuous quality improvement.
Collaborate with full-stack and frontend engineers to integrate AI capabilities into enterprise applications and user-facing solutions.
Work closely with QA teams on AI-output quality testing and validation.
Develop APIs, microservices, and integrations supporting AI capabilities within enterprise environments.
Deploy, maintain, and optimize AI components and services in Azure environments.
Monitor AI solution performance, reliability, quality, scalability, and operational costs.
Prepare architecture diagrams, technical documentation, design documents, READMEs, and implementation guidelines.
Contribute to reusable AI engineering practices, frameworks, and delivery standards across the organization.
Take ownership of technical deliverables and work independently with minimal supervision while collaborating effectively within cross-functional teams.
Required Experience and Skills:
Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or a related field.
5+ years of experience in Software Engineering, Data Engineering, or related technical roles.
2+ years of hands-on experience building and deploying LLM-powered applications in production environments.
Strong Python programming skills with experience delivering clean, maintainable, production-grade software.
Hands-on experience with LLM application and agent frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar technologies.
Proven experience developing production-grade agentic AI solutions, including multi-agent systems, tool integration, autonomous workflows, and agent orchestration.
Hands-on experience with Microsoft Copilot Studio, including building agents/copilots, configuring topics and actions, and integrating with enterprise data sources.
Strong understanding of Retrieval-Augmented Generation (RAG), including document chunking, embeddings, retrieval strategies, and evaluation techniques.
Experience with vector databases and semantic search technologies such as Azure AI Search, pgvector, Pinecone, or Weaviate.
Strong understanding of prompt engineering and structured output design.
Experience with AI evaluation and observability using tools such as LangSmith, Langfuse, or in-house evaluation frameworks, including offline and online quality measurement.
Understanding of multi-agent architectures and orchestration patterns such as planner/executor and supervisor approaches.
Hands-on experience with Microsoft Azure, Azure AI Fo
