Description

You will design and implement enterprise-grade GenAI solutions for customer projects.

Responsibilities

• Develop and operationalize Retrieval-augmented generation (RAG) pipelines combining LLMs, embeddings, and vector databases.
• Design and implement agentic AI workflows using tools like LangChain, Semantic Kernel, or LlamaIndex.
• Integrate GenAI components with enterprise APIs, data stores, and applications (.NET, Power Platform, or web).
• Fine-tune and evaluate LLMs using Azure OpenAI, OpenAI, or Anthropic models.
• Optimize prompts, retrieval logic, and caching for accuracy, security, and cost efficiency.
• Collaborate with data engineers, solution architects, and customer teams to deliver production-ready AI solutions.

Required Skills

• 8–12 years overall software experience with 3+ years in applied AI/ML or LLM development.
• Proficiency in Python, LangChain, Semantic Kernel, and LLM APIs.
• Solid understanding of RAG architectures, agent orchestration, and prompt engineering.
• Experience deploying GenAI workloads on Azure (Cognitive Search, OpenAI, Functions) or Databricks.
• Knowledge of data pipelines, API integration, and cloud security.
• Experience with vector databases such as Pinecone, FAISS, or Chroma.
• Familiarity with .NET or Power Platform integration.

Key Skills

Education

Any Graduate