Senior AI Engineer Agentic AI / Generative AI / LLM

Position Overview

We are seeking an experienced Senior AI Engineer to design, develop, deploy, and support enterprise-grade Generative AI and Agentic AI solutions.

Key Responsibilities

• Design and develop enterprise-grade Agentic AI and LLM-based applications using frameworks such as LangChain and LangGraph.
• Build multi-step LLM workflows, including orchestration, tool/function calling, structured outputs, retries, and error handling.
• Develop and optimize RAG pipelines, embeddings, vector search, indexing, and retrieval strategies.
• Design and implement LLM evaluation frameworks, golden datasets, quality metrics, and regression testing.
• Develop scalable AI applications and APIs using Python 3.11+, FastAPI, and Pydantic.
• Implement AI observability, tracing, structured logging, performance monitoring, and cost optimization using Datadog and OpenTelemetry.
• Deploy and maintain production AI applications, ensuring reliability, scalability, security, and performance.
• Conduct model benchmarking, fine-tuning, experimentation, and optimization.
• Collaborate with Product, Business, Risk, Compliance, and Security teams to translate business requirements into AI solutions.
• Provide production support, incident management, root-cause analysis, and continuous improvement.
• Mentor junior engineers and contribute to architecture, code reviews, and engineering best practices.

Required Technical Skills

• Agentic AI & LLM Frameworks: LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, or Amazon Bedrock Agents.
• Generative AI / LLMs: Enterprise LLM application development, orchestration, prompt engineering, and tool calling.
• RAG: Embeddings, vector databases, indexing, retrieval optimization, and document-based AI solutions.
• Programming: Strong Python 3.11+, asynchronous programming, type annotations, automated testing, FastAPI, and Pydantic.
• LLM Evaluation: Golden datasets, evaluation metrics, LLM-as-a-Judge, evaluation frameworks, and regression testing.
• Cloud & AI Platforms: Hands-on experience with enterprise cloud AI services, preferably Azure AI technologies and/or AWS AI services.
• Observability: Datadog, OpenTelemetry, tracing, logging, AI quality monitoring, and cost monitoring.
• Database: PostgreSQL, Aurora PostgreSQL, and vector database extensions.
• DevOps: Kubernetes, Helm, Terraform, CI/CD, and Apache Kafka.

Mandatory Experience

• 8+ years of professional software engineering experience.
• 3+ years of hands-on Generative AI / LLM application development.
• Strong implementation experience in Agentic AI and LLM orchestration, particularly LangChain.
• Demonstrated experience deploying at least one enterprise-grade GenAI/LLM application into production.
• Experience supporting live production AI applications, including incident response, performance optimization, and regression management.
• Strong understanding of enterprise security, access control, data governance, and compliance requirements.

Preferred Qualifications

• Experience in Financial Services, Banking, Insurance, Healthcare, or other regulated industries.
• Strong Azure AI implementation experience.
• Document AI / Intelligent Document Processing, OCR, and structured data extraction.
• Human-in-the-loop AI workflows and confidence-based automation.
• Experience with model benchmarking, fine-tuning, distillation, and optimization.
• Experience designing scalable, event-driven enterprise AI architectures.