Artificial Intelligence Engineer - SA

V2 Solutions; Hyderabad; On-site; INR 1,400,000 - 2,600,000; Full time

Job summary

V2 Solutions in Hyderabad, India seeks a Senior Associate AI Engineer to own features end-to-end across a React front-end, Python FastAPI services, and GenAI agent logic powering them. You will design agent workflows, mentor associates, and partner with product and business teams to automate HR, Payroll, SAP, and Client Delivery using LangChain, LangGraph, and CrewAI.

You will build robust, observable workflows with secure integrations to enterprise systems, deliver reusable components, and help

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.
• 4–8 years building and deploying production-grade full-stack and/or AI/ML solutions.
• Strong proficiency in Python (FastAPI, REST APIs) and React/TypeScript front-end development.
• Hands-on experience with agentic frameworks (LangChain, LangGraph, CrewAI, ReAct, or similar).
• Solid grasp of LLM orchestration — prompt design, tool usage, context management, and agent memory.
• Experience integrating enterprise systems via APIs, event/message queues (Kafka, Service Bus), and webhooks.
• Foundations in distributed system design — state management, retries, error handling, and resilience.
• Comfortable with SQL/NoSQL , data modeling, containerization (Docker) , orchestration ( Kubernetes ), and cloud ( Azure / AWS / GCP ).
• Understanding of security — authentication (OAuth2), authorization, and secure API integration.
• Experience mentoring engineers and reviewing others’ code.

Responsibilities

• Design and build full-stack features end-to-end — React/TypeScript front-ends and Python/FastAPI back-ends for GenAI agent products
• Architect and implement LLM-powered agent workflows using LangChain, LangGraph, or CrewAI to automate managed-services business processes
• Develop prompt chains, agent tools, memory/state handling, and fallback logic for multi-step task execution
• Build and optimize RAG pipelines, vector store integrations , and knowledge-retrieval techniques
• Integrate agents with enterprise systems (Workday, SAP, Salesforce) via REST APIs, SDKs, message queues, and webhooks
• Design robust front-end UX for agent interactions — streaming responses, human-in-the-loop , and observability dashboards
• Ensure workflows are robust, observable, and secure with proper logging, monitoring, exception handling, and OAuth2
• Contribute to deployment, testing, and validation automation , and uphold code quality through reviews
• Mentor associate engineers and collaborate with PMs and SMEs to translate business processes into agentic workflows
• Own documentation of design rationale, agent behavior, and integration requirements

Skills

Python

React/TypeScript

LLM orchestration

LangChain

LangGraph

CrewAI

REST APIs

OAuth2

SQL/NoSQL

Docker

Kubernetes

Cloud (Azure/AWS/GCP)

Distributed systems

ReAct

Education

Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field

Tools

LangChain

LangGraph

CrewAI

ReAct

FastAPI

React

Kafka

Service Bus

Webhooks

Docker

Kubernetes

Azure

AWS

GCP

Job description

Role & responsibilities

Senior Associate AI Engineer (Full Stack) End-to-end feature ownership across React + Python with agent design Experience: 4-8 years

About the Role

We are seeking a Senior Associate AI Engineer to own features end-to-end on our AI Ops delivery team from the React front-end, through Python (FastAPI) services, to the GenAI agent logic that powers them. You will design agent workflows, make sound technical decisions, mentor associates, and partner with product and business teams to automate processes across HR, Payroll, SAP, and Client Delivery using frameworks like LangChain , LangGraph , and CrewAI .

Key Responsibilities

• Design and build full-stack features end-to-end — React/TypeScript front-ends and Python/FastAPI back-ends for GenAI agent products
• Architect and implement LLM-powered agent workflows using LangChain, LangGraph, or CrewAI to automate managed-services business processes
• Develop prompt chains, agent tools, memory/state handling, and fallback logic for multi-step task execution
• Build and optimize RAG pipelines, vector store integrations , and knowledge-retrieval techniques
• Integrate agents with enterprise systems (Workday, SAP, Salesforce) via REST APIs, SDKs, message queues, and webhooks
• Design robust front-end UX for agent interactions — streaming responses, human-in-the-loop , and observability dashboards
• Ensure workflows are robust, observable, and secure with proper logging, monitoring, exception handling, and OAuth2
• Contribute to deployment, testing, and validation automation , and uphold code quality through reviews
• Mentor associate engineers and collaborate with PMs and SMEs to translate business processes into agentic workflows
• Own documentation of design rationale, agent behavior, and integration requirements

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field
• 4–8 years building and deploying production-grade full-stack and/or AI/ML solutions
• Strong proficiency in Python (FastAPI, REST APIs, background orchestration) and React/TypeScript front-end development
• Hands-on experience with agentic frameworks (LangChain, LangGraph, CrewAI, ReAct, or similar)
• Solid grasp of LLM orchestration — prompt design, tool usage, context management, and agent memory
• Experience integrating enterprise systems via APIs, event/message queues (Kafka, Service Bus), and webhooks
• Foundations in distributed system design — state management, retries, error handling, and resilience
• Comfortable with SQL/NoSQL , data modeling, containerization (Docker) , orchestration ( Kubernetes ), and cloud ( Azure / AWS / GCP )
• Understanding of security — authentication (OAuth2), authorization, and secure API integration
• Experience mentoring engineers and reviewing others’ code

Nice to Have

• Experience with multi-agent patterns (supervisor-worker, planner-executor, judge-critic)
• Contributions to open-source agent frameworks or AI tooling
• Experience with observability and monitoring tools to track agent performance
• Exposure to knowledge graphs, advanced memory management, or advanced RAG pipelines