Sr AI Developer

Role Overview:

We are seeking a highly skilled and innovative Senior AI Developer with deep expertise in building AI Agents and Retrieval-Augmented Generation (RAG) applications. The ideal candidate will have hands-on experience with a variety of GenAI frameworks and exposure to MS Copilot Studio and Power Automate enabling seamless integration of intelligent workflows across enterprise platforms.

Key Responsibilities:

• Design develop and deploy advanced AI Agents capable of autonomous task execution contextual understanding and conversational interactions.
• Architect and implement RAG pipelines using vector databases embedding models and document retrieval systems to enhance LLM capabilities.
• Develop and integrate GenAI solutions with Copilot Studio and Power Automate to build intelligent business process automations.
• Collaborate with cross-functional teams including data scientists product managers and business stakeholders to translate requirements into scalable AI solutions.
• Optimize performance latency and accuracy of GenAI models in production environments.
• Stay current with the latest advancements in LLMs agentic workflows and GenAI tooling to continuously improve solution quality.
• Mentor junior developers and contribute to internal knowledge sharing and best practices.

Required Qualifications:

• Bachelors or Masters degree in Computer Science Artificial Intelligence or related field.
• 5 years of experience in AI/ML development with at least 2 years focused on Generative AI.
• Hands-on experience with Copilot Studio Power Automate Dataverse Microsoft Fabric for building intelligent workflows and business applications.
• Hands-on experience in Azure AI Services like Azure Open AI Service Azure AI Studio and Azure services like Azure Functions Azure Kubernetes Service Azure Blob Storage Azure Cosmos DB Azure Bot Service Azure SDKs.
• Proven experience in building AI Agents using frameworks like LangChain LangGraph Semantic Kernel Haystack or similar.
• Strong understanding of RAG architecture including document chunking embeddings vector stores (e.g. FAISS Pinecone Weaviate) and LLM integration.
• Proficiency in Python JavaScript frameworks REST API development and MongoDB.
• Experience with cloud platforms (Azure AWS GCP) and deploying GenAI solutions at scale.
• Familiarity with prompt engineering model fine-tuning evaluation techniques and building guardrails.

• Experience in building enterprise-grade GenAI platforms.
• Knowledge of enterprise data governance security and compliance in AI applications.
• Exposure to MLOps and CI/CD pipelines for AI model deployment.
• Excellent communication and stakeholder management skills.