AI Engineer - Agentic AI & LLMOps

AI Engineer - Agentic AI & LLMOps

About StratLytics

StratLytics is a data science, AI, and analytics consulting firm building production-grade analytics and AI solutions for enterprise clients across financial services, fin-tech, risk analytics, manufacturing, retail, and other sectors. We combine data engineering, machine learning, Generative AI, decision intelligence, and business workflow automation to solve high-impact business problems.

Role Overview

We are hiring an AI Engineer - Agentic AI & LLMOps to support a financial-services AI initiative in North America.

This is a hands-on applied AI engineering role focused on building production-grade AI systems, not generic chatbot demos. The engineer will work on backend APIs, Agentic AI workflows, LLM orchestration, structured JSON outputs, evaluation pipelines, audit logging, and deployment-ready AI services.

The ideal candidate should have strong Python backend engineering skills, practical experience with LLM APIs, and the ability to build reliable, testable, maintainable AI applications.

Key Responsibilities

• Build backend services using Python, FastAPI, and Pydantic.
  
• Develop Agentic AI workflows using LangGraph, LangChain, AutoGen, or similar frameworks.
  
• Integrate LLM APIs such as OpenAI, Azure OpenAI, or AWS Bedrock.
  
• Design structured JSON outputs for AI-generated recommendations.
  
• Implement prompt versioning, schema validation, response validation, and logging.
  
• Build multi-step AI workflows involving specialist agents and synthesis logic.
  
• Implement timeout handling, retry logic, fallback behavior, and error management.
  
• Work with data engineers to consume validated and normalized business data payloads.
  
• Build audit logging, traceability, and observability into AI workflows.
  
• Support evaluation and regression testing of prompts, agents, and outputs.
  
• Containerize and deploy backend services using Docker and CI/CD workflows.
  
• Collaborate with data scientists, AI scientists, data engineers, and business SMEs.
  
• Write clean, modular, maintainable, and well-tested code.
  

Ideal Candidate Profile

The ideal candidate is a strong Python backend engineer who has moved into applied AI engineering. They should understand that production AI is not just prompt writing. It requires API design, schema validation, error handling, observability, testing, deployment discipline, and secure handling of business-critical data.

The candidate should be comfortable building AI systems where outputs must be structured, traceable, review-able, and reliable.

• 4–5 years of hands-on software engineering experience
  
• Strong programming skills in Python
  
• Hands-on experience with FastAPI or Flask
  
• Experience with Pydantic, JSON Schema, or structured data validation
  
• Good understanding of REST API design and backend service development
  
• Practical experience integrating LLM APIs (OpenAI, Azure OpenAI, AWS Bedrock, or similar)
  
• Working knowledge of PostgreSQL or similar relational databases
  
• Experience with Docker and containerised application development
  
• Strong Git / GitHub workflow
  
• Ability to write clean, modular, maintainable, and testable code
  
• Ability to work from StratLytics' Bhubaneswar office, 5 days a week
  

• Exposure to LangChain, LangGraph, AutoGen, LlamaIndex, or similar Agentic AI frameworks
  
• Experience with structured AI outputs, function calling, tool calling, or schema-constrained responses
  
• Experience with logging, error handling, debugging, and unit testing
  
• Understanding of LLMOps concepts (prompt versioning, evaluation, observability, regression testing)
  
• Prior exposure to financial services, fin-tech, credit risk, compliance, or risk analytics