As a Senior AI Engineer, you will design, build, deploy, and support production-grade GenAI and agentic AI solutions that integrate large language models (LLMs), retrieval-based patterns, APIs, and enterprise workflows. You will play a hands-on engineering role in delivering scalable, reliable, and maintainable AI-powered product capabilities, while partnering closely with Lead AI Engineers, Team Leads, architects, and cross-functional product teams.

This role is ideal for an engineer with strong technical depth in LLM-powered application development, RAG, and cloud-native AI delivery, who can independently implement solution components, contribute to engineering standards, and help operationalize AI systems in real business environments.

5 to 8+ years of experience in software engineering, AI/ML engineering, or AI solution delivery, including hands-on work in building and deploying intelligent applications

Practical experience delivering GenAI, LLM-powered, or AI-enabled solutions in development, pilot, or production environments

Strong technical foundation in Python and modern backend engineering patterns, with experience building APIs, services, and application components

Hands-on experience with LLM platforms and AI development tools such as Azure OpenAI, Azure AI Studio, OpenAI API, AWS Bedrock, Google Vertex AI, or equivalent

Experience working with orchestration frameworks such as Semantic Kernel, LangChain, AutoGen, or equivalent approaches for prompt workflows, tool calling, 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, FAISS, or equivalent

Experience building and deploying cloud-native AI services using tools such as Azure Functions, Azure Container Apps, FastAPI, Docker, GitHub, Azure DevOps, or equivalent engineering and deployment platforms

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, or equivalent monitoring platforms

Experience integrating AI services with REST APIs, enterprise workflows, backend systems, or downstream business applications

Strong problem-solving skills and ability to translate solution requirements into well-structured technical implementations

Strong ownership mindset across the SDLC, including design, build, testing, deployment, support, and continuous improvement

Good collaboration and communication skills, with the ability to work effectively with engineers, architects, product owners, and platform teams

Design, develop, test, and deploy LLM-powered application components and AI-enabled services for enterprise use cases

Build and optimize retrieval-augmented generation (RAG) pipelines, including document ingestion, chunking, embeddings, retrieval strategies, and response grounding

Implement agentic AI workflows using orchestration frameworks and reusable design patterns for task execution, tool usage, and context handling

Develop AI-enabled APIs and backend services using technologies such as Python, FastAPI, Azure Functions, containerized services, and REST-based integration patterns (or equivalent platforms and frameworks)

Work with Azure OpenAI, Azure AI Studio, Semantic Kernel, LangChain, AutoGen, Azure AI Search, or equivalent tools to build scalable GenAI solutions

Collaborate with Lead AI Engineers and architects to translate solution designs into robust technical implementations

Integrate AI services with enterprise systems, APIs, workflow platforms, and downstream applications

Implement logging, tracing, monitoring, and basic operational controls using tools such as Application Insights, OpenTelemetry, Azure Monitor, Datadog, New Relic, or equivalent observability platforms

Participate in design reviews, code reviews, testing, and release activities to maintain quality and engineering discipline

Contribute to reusable assets such as prompt patterns, orchestration templates, shared components, developer utilities, and engineering accelerators

Troubleshoot production issues, improve reliability, and support continuous improvement of deployed AI capabilities

Stay current with advancements in LLM tooling, agent frameworks, prompt engineering, retrieval approaches, and applied AI engineering practices