Description

You will design, develop, and deploy scalable agentic AI systems and LLM-based applications. This role combines strong software engineering fundamentals with applied AI/ML expertise to build production-grade autonomous workflows.

This role is remote.

Responsibilities

• Design and implement intelligent AI agents capable of reasoning, planning, tool usage, and multi-step workflow execution using frameworks like LangChain, LangGraph, CrewAI, or AutoGen.
• Build and optimize retrieval-augmented generation (RAG) pipelines that integrate vector databases with enterprise knowledge systems.
• Develop scalable backend services and APIs using Python, Java, or Go, deploying them as cloud-native microservices on AWS, Azure, or GCP.
• Containerize applications with Docker and Kubernetes, implementing CI/CD pipelines and infrastructure-as-code practices.
• Lead technical design discussions, conduct architecture reviews, and mentor junior engineers on AI engineering best practices.

Required Skills

• 7+ years of software engineering experience, including at least 2 years building generative AI or LLM-based applications.
• Strong programming proficiency in Python and at least one additional language such as Java, Go, or TypeScript.
• Hands-on experience with LLM orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel).
• Experience with vector databases (Pinecone, Weaviate, FAISS, ChromaDB) and RAG architectures.
• Proficiency with cloud platforms (AWS, Azure, GCP) and MLOps practices.
• Strong understanding of distributed systems, microservices, REST APIs, and system scalability.
• Bachelor’s or Master’s degree in Computer Science, Engineering, AI/ML, or a related field.

Preferred Skills

• Experience with multi-agent orchestration systems and AI governance.
• Familiarity with reinforcement learning, model fine-tuning, or evaluation frameworks.
• Contributions to open-source AI projects or published technical research.

Key Skills

Education

Any Graduate