Senior AI Engineer

We are seeking a Senior AI Engineer to design and build scalable AI solutions with a strong focus on Agentic AI, RAG systems, and production-grade LLM applications. The role emphasizes hands‑on development, practical system design, and reliable production deployment, along with structured evaluation and observability practices.

Responsibilities

• Design and implement RAG‑based AI systems with hybrid search, embeddings, and optimized retrieval strategies.
• Build and deploy AI agents using modern agentic AI frameworks.
• Develop multi‑step workflows and tool‑using agents for real‑world business use cases.
• Build conversational AI systems with context handling and multi‑turn interactions.
• Design scalable AI services for real‑time and batch use cases.
• Design and implement solutions using at least one of the following frameworks:
  • LangGraph
  • Semantic Kernel
  • AutoGen
  • CrewAI
  • OpenAI Agents SDK
• Demonstrate working knowledge of multiple agent frameworks and their trade‑offs.
• Apply prompt engineering, few‑shot learning, and retrieval techniques to improve LLM performance.
• Build reusable components for agent orchestration, tool usage, and workflow design.
• Design and implement semantic search and vector‑based retrieval systems.
• Hands‑on experience with Azure AI Search and PostgreSQL (pgvector).
• Optimize embedding strategies, indexing, and query performance.
• Implement LLM evaluation approaches (offline testing, basic automated evaluation pipelines).
• Define and track quality metrics such as accuracy, relevance, and response quality.
• Apply techniques for hallucination detection and mitigation.
• Build basic monitoring and observability for AI systems (logs, traces, performance metrics).
• Continuously improve model performance based on evaluation insights.
• Develop APIs and microservices for integrating AI systems.
• Work with cloud platforms (Azure/AWS/GCP) and containerization (Docker).
• Contribute to deployment pipelines and production readiness.
• Prototype and validate solutions through POCs and pilots.

Requirements

• 6–9 years of experience in AI/ML.
• Strong Python skills and experience with ML frameworks (PyTorch or TensorFlow).
• Practical experience building LLM‑based applications and RAG pipelines.
• Hands‑on experience with at least one agentic AI framework:
  • LangGraph, Semantic Kernel, AutoGen, CrewAI, or OpenAI Agents SDK.
• Familiarity with multi‑agent workflows and orchestration patterns.
• Experience with vector databases and semantic search systems.
• Hands‑on exposure to Azure AI Search and pgvector (PostgreSQL).
• Understanding of embeddings and similarity search.
• Experience or strong understanding of:
  • LLM evaluation techniques.
  • Output quality measurement.
  • Hallucination detection approaches.
  • Monitoring and observability concepts.
• Agentic AI frameworks: LangGraph, Semantic Kernel, AutoGen, CrewAI, OpenAI Agents SDK.
• RAG frameworks: LangChain, LlamaIndex.
• Vector databases: Azure AI Search, pgvector.
• LLM/ML: PyTorch, OpenAI APIs.
• Serving: FastAPI.
• Infrastructure: Azure/AWS/GCP, Docker.
• Monitoring: LangSmith (or similar).
• Languages: Python.