Generative AI Engineer

4–10+ 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