Role Summary
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.
Key Responsibilities
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
Required Qualifications
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
Preferred Qualifications
Experience implementing agentic AI workflows involving multi-step reasoning, tool orchestration, structured prompting, or reusable workflow patterns
Exposure to Model Context Protocol (MCP), agent-to-agent (A2A) interaction patterns, or similar approaches to context exchange and distributed agent communication
Familiarity with Microsoft AI Foundry, Azure Machine Learning, Azure AI / Copilot Studio, or equivalent enterprise AI experimentation and solution development platforms
Experience with enterprise integrations, including workflow tools, API management layers, business systems, or event-driven architectures
Experience contributing to reusable GenAI accelerators, prompt libraries, orchestration templates, internal developer tooling, or shared engineering utilities
Familiarity with AI governance, safety, evaluation, and cost-management practices, including token usage awareness, prompt safety, and quality monitoring
Working knowledge of TypeScript or C#, in addition to Python, for integration into broader enterprise technology stacks
Experience operating in a build-own-operate product environment with expectations around supportability, reliability, and iterative enhancement
Ability to clearly communicate technical decisions, implementation trade-offs, and design considerations to both technical and non-technical stakeholders
