Senior Artificial Intelligence Engineer

Role & 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.

Preferred candidate profile

• Bachelors or Master’s 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.