Shape the future of AI by designing, building, and deploying production-grade Generative AI and agentic AI solutions. This role involves integrating large language models (LLMs), retrieval patterns, APIs, and enterprise workflows to deliver scalable and reliable AI-powered product capabilities. You'll collaborate with lead engineers and product teams, contributing hands-on technical expertise to complex solution components and engineering standards.

This opportunity is perfect for engineers with deep technical knowledge in LLM application development, RAG, Python, SQL, and cloud-native AI delivery. You will independently manage intricate solution parts, foster engineering best practices, guide junior engineers, and help operationalize AI systems within real business contexts.

Independently design, develop, test, and deploy sophisticated LLM-powered application components and AI-enabled services for enterprise use cases. Build and optimize retrieval-augmented generation (RAG) pipelines, focusing on document ingestion, chunking, embeddings, retrieval strategies, and response grounding.

Implement agentic AI workflows utilizing orchestration frameworks and reusable design patterns for task execution, tool usage, and context management. Develop AI-enabled APIs and backend services using Python, FastAPI, Azure Functions, containerized services, and REST-based integration patterns. Utilize tools like Azure OpenAI, Azure AI Studio, Semantic Kernel, LangChain, AutoGen, and Azure AI Search to construct scalable GenAI solutions.

Collaborate with lead AI engineers and architects to translate solution designs into robust technical implementations, offering guidance on implementation choices. Integrate AI services with enterprise systems, APIs, workflow platforms, and downstream applications. Implement comprehensive logging, tracing, monitoring, and operational controls using platforms such as Application Insights, OpenTelemetry, Azure Monitor, Datadog, or New Relic.

Actively participate in design and code reviews, testing, and release activities to uphold quality and engineering discipline. Contribute to reusable assets like prompt patterns, orchestration templates, shared components, and developer utilities. Troubleshoot production issues, enhance reliability, and support the continuous improvement of deployed AI capabilities. Stay abreast of advancements in LLM tooling, agent frameworks, prompt engineering, retrieval approaches, and applied AI engineering practices.

A minimum of 5 to 8 years of experience in software engineering, AI/ML engineering, or AI solution delivery, with a proven track record in building and deploying intelligent applications. Possess hands-on experience with Snowflake, Databricks, or equivalent data platforms.

Demonstrate practical experience in delivering GenAI, LLM-powered, or AI-enabled solutions across development, pilot, or production environments. Maintain a strong technical foundation in Python and modern backend engineering patterns, including building APIs, services, and application components. Experience with LLM platforms and AI development tools such as Azure OpenAI, Azure AI Studio, OpenAI API, AWS Bedrock, Google Vertex AI, or Databricks is essential.

Proficiency in working with orchestration frameworks like Semantic Kernel, LangChain, or AutoGen for prompt workflows and agent coordination. Strong working knowledge of retrieval-augmented generation (RAG), embeddings, vector search, and grounding patterns using platforms such as Azure AI Search, Pinecone, Weaviate, or FAISS. Experience building and deploying cloud-native AI services using tools like Azure Functions, Azure Container Apps, FastAPI, Docker, GitHub, or Azure DevOps.

Possess a solid understanding of CI/CD, containerization, automated testing, and secure deployment practices for modern AI-enabled applications. Familiarity with observability and operational tooling such as Application Insights, OpenTelemetry, Azure Monitor, Datadog, or New Relic. Experience integrating AI services with REST APIs, enterprise workflows, backend systems, or downstream business applications. Exhibit strong problem-solving skills and the ability to translate requirements into well-structured technical implementations. Maintain a strong ownership mindset across the full SDLC for assigned features and services. Excellent collaboration and communication skills are crucial for effective teamwork.