Agentic AI Engineer (LLM / RAG / Python) - Hybrid

We’re looking for a hands-on Agentic AI Engineer to design, build, and deploy production-grade AI agents, LLM-powered applications, RAG solutions, and AI-enabled workflows for a major financial services client. Agentic AI and AI engineering are the core of this role. We want someone who has taken AI solutions beyond prototypes and into production, backed by a solid data engineering foundation., * Design, develop, and deploy production-grade Agentic AI and LLM-based solutions

• Build AI agents that handle reasoning, retrieval, tool invocation, and multi-step workflows
• Design and implement RAG solutions using enterprise data and knowledge sources
• Develop prompt strategies, reusable templates, and context-management approaches
• Build backend AI services and APIs in Python
• Integrate LLMs and agents with enterprise applications, APIs, databases, and external tools, with proper authentication, authorization, and security controls
• Develop retrieval pipelines covering document processing, embeddings, vector search, and contextual retrieval
• Implement evaluation frameworks for retrieval quality, response quality, and agent behavior
• Deploy to cloud environments and support production operations, including monitoring, logging, tracing, alerting, and observability
• Troubleshoot production AI systems and improve accuracy, reliability, and latency
• Build and maintain the data ingestion and transformation pipelines AI applications depend on
• Collaborate with data engineers, data scientists, software engineers, architects, security teams, and business stakeholders
• Rapidly build POCs/MVPs that can evolve into secure, scalable production solutions

• 2+ years of hands-on AI engineering experience building LLM, Generative AI, or Agentic AI solutions
• 2+ years of strong hands-on experience with Agentic AI, LLMs, prompt engineering, and RAG
• 2+ years of production development with Python, APIs, cloud platforms, and enterprise security practices
• Delivered at least one complete production AI solution covering retrieval, tool execution, evaluation, deployment, and monitoring
• Solid data engineering experience: ingestion, transformation, management, and delivery
• Cloud experience on AWS, Azure, or Google Cloud Platform

Preferred Qualifications

• Agent frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or Strands
• AWS Bedrock, Azure AI Foundry/OpenAI, Anthropic, OpenAI, or similar enterprise AI platforms
• Knowledge of Model Context Protocol (MCP)
• Vector databases and enterprise search platforms
• LLM/agent evaluation, tracing, and observability experience
• AI guardrails, prompt-injection protection, audit logging, and Responsible AI practices
• Databricks, Spark/PySpark, Snowflake, or similar data platforms
• Strong software engineering, CI/CD, and cloud-native development background
• Banking or financial services experience