AI Engineer

Hr Actowiz Solutions; Ahmedabad District; On-site; INR 900,000 - 1,300,000; Full time

Job summary

Actowiz Solutions in Ahmedabad is seeking an AI Engineer to design and deploy production-ready AI agents and multi-agent workflows that automate complex business processes. You will work at the intersection of LLM engineering and backend development, using a modern stack centered on LangGraph, PydanticAI, and Model Context Protocol to deliver reliable, scalable AI services.

This hands-on role offers opportunities to build real-world AI systems, with emphasis on validation, reliability, and

Qualifications

• 3-6 years of software/ML/AI engineering experience.
• Hands-on experience building production Python applications.
• Experience with LangChain or LangGraph (LLM frameworks).
• Strong experience with Pydantic and structured data validation.
• Exposure to multi-agent frameworks such as CrewAI is a plus.
• Experience with LLM APIs and prompt engineering.
• Familiarity with RAG pipelines and vector databases.

Responsibilities

• Design and implement stateful AI workflows using LangGraph.
• Build role-based multi-agent collaborations using CrewAI.
• Develop reliable long-running and branching AI processes.
• Use Pydantic to enforce schema-driven outputs and validation layers.
• Contribute to reliability, logging, and observability of AI services.
• Work with local models (Ollama / LM Studio) and cloud LLMs.

Skills

Python

LangGraph

Pydantic

LangChain

CrewAI

LLM APIs

Prompt engineering

RAG pipelines

Vector DB

Docker

Cloud/serverless

REST

gRPC

Asyncio

Neo4j

Tools

Qdrant

PgVector

Neo4j

LangGraph

PydanticAI

Job description

AI Engineer

Actowiz Solutions

3-6 Years

Ahmedabad

Full-Time

Role Summary

We are looking for a highly skilled AI Engineer to help build and scale our internal AI ecosystem. You will design and deploy production-ready AI agents and multi-agent workflows that automate complex business processes.

This role bridges LLM engineering and backend software development, using a modern stack centered on LangGraph, PydanticAI, and Model Context Protocol (MCP).

This is a hands-on role ideal for someone excited about building real-world AI systems and shipping them to production.

Agentic Workflow Development

• Design and implement stateful AI workflows using LangGraph.


• Build role-based multi-agent collaborations using CrewAI.


• Develop reliable long-running and branching AI processes.

Structured AI Services

• Use Pydantic and PydanticAI to enforce type safety and structured outputs.


• Implement schema-driven AI pipelines and validation layers.


• Contribute to reliability, logging, and observability of AI services.

RAG & Context Engineering

• Build and maintain Retrieval-Augmented Generation (RAG) pipelines.


• Work with vector databases such as Qdrant, ChromaDB, or PgVector.


• Contribute to GraphRAG implementations using Neo4j or FalkorDB.


• Improve search quality using hybrid search and reranking techniques.

MCP & Internal Tooling

• Help build and maintain Model Context Protocol (MCP) servers.


• Integrate AI agents with internal APIs, databases, and tools.


• Support development of internal AI frameworks and reusable components.

Model Integration & Optimization

• Work with both local models (Ollama / LM Studio) and cloud LLM providers.


• Assist in model evaluation, optimization, and experimentation.


• Support domain-specific fine-tuning and benchmarking.

Performance & Scalability

• Implement semantic caching and context optimization strategies.


• Improve latency, cost efficiency, and scalability of AI services.

Deployment & Engineering

• Containerize services using Docker.


• Deploy AI workloads on AWS Lambda or GCP Cloud Functions.


• Write clean, maintainable, production-quality Python code.

Required Skills & Experience

• 3-6 years of experience in software engineering, ML engineering, or AI engineering.


• Hands-on experience building production applications in Python.


• Experience with LangChain or LangGraph (or similar LLM frameworks).


• Strong experience with Pydantic and structured data validation.


• Exposure to multi-agent frameworks such as CrewAI is a plus.


• Experience working with LLM APIs and prompt engineering.


• Familiarity with RAG pipelines and vector databases.

Databases

• Experience with at least one Vector DB (Qdrant / PgVector) or Graph DB (Neo4j or FalkorDB).

Backend & Infrastructure

• Strong Python (Asyncio preferred).


• Experience with Docker and cloud/serverless deployments.


• Understanding of REST or gRPC APIs.

Preferred Qualifications

• Experience with Human-in-the-Loop workflows.


• Background in semantic search or information retrieval.


• Experience building internal tools or developer platforms.


• Familiarity with model fine-tuning or evaluation.