Role Overview
We are looking for a hands-on AI Engineer to design, develop, and deploy AI-powered applications leveraging LLMs, Agentic AI frameworks, and modern AI engineering tools. The ideal candidate should have practical experience building AI agents, copilots, and RAG-based solutions that solve real business problems and create measurable value.
Key Responsibilities
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Design and develop AI agents, copilots, and autonomous workflows.
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Build solutions using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, and Google ADK.
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Develop RAG solutions, semantic search capabilities, and enterprise knowledge assistants.
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Integrate AI solutions with business applications, APIs, databases, and enterprise systems.
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Leverage AI-assisted development tools such as Claude Code, Cursor, and GitHub Copilot.
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Package, deploy, and monitor AI applications in cloud environments.
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Contribute reusable frameworks, accelerators, and AI engineering best practices.
Required Technical Skills
Programming & Engineering
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Strong proficiency in Python.
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Experience with REST APIs and integrations.
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FastAPI, Flask, or similar frameworks.
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Strong software engineering and system design fundamentals.
Agentic AI & LLMs
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Hands-on experience with one or more:
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LangChain
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LangGraph
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CrewAI
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AutoGen
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Google ADK
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Prompt Engineering
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Function/Tool Calling
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Agent Workflows and Memory Concepts
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Building LLM-powered applications
RAG & Knowledge Systems
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Retrieval-Augmented Generation (RAG)
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Vector Databases (Pinecone, Chroma, Weaviate, Milvus, etc.)
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Semantic Search and Embeddings
Cloud & Deployment
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Docker
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Azure, AWS, or GCP
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CI/CD fundamentals
Preferred Skills
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Model Context Protocol (MCP)
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Multi-Agent Systems
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GraphRAG
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Semantic Kernel
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OpenAI Agents SDK
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PydanticAI
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AI Evaluation Frameworks (Promptfoo, RAGAS)
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Knowledge Graphs
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React or Streamlit-based AI applications
Professional Attributes (Mandatory)
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Strong collaborator capable of working effectively across engineering, product, business, and leadership teams.
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Excellent communication skills with the ability to explain technical concepts to diverse audiences.
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Confident, proactive, and self-driven individual who takes ownership and delivers results.
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Strong networking and relationship-building abilities, with the capability to engage stakeholders across teams and geographies.
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Comfortable conducting demos, presentations, workshops, and technical discussions.
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Passionate about continuous learning and staying current with emerging AI technologies.
Experience Requirements
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3–8 years of overall software engineering experience.
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Minimum 2 years of hands-on experience in Generative AI, Agentic AI, or LLM-based application development.
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Experience building and delivering AI-powered applications, agents, copilots, or automation solutions in enterprise or production environments.
Total Experience Expected: 02-04 years
