actowiz is hiring a AI Engineer — a full-time, based in Ahmedabad, India role. Apply directly on actowiz's careers page below.
AI Engineer
Location: Ahmedabad, India
Department: Operations
Experience: 3-6 Years
Skills: Qdrant / PgVector, LLM APIs, prompt engineering, AI, docker, FalkorDB, RAG pipelines, CrewAI, webscraping, python
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.
Key Responsibilities
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.
