Job Title: Agentic AI Engineer / LLM Engineer
Location: India (Hybrid)
Experience: 4–8 Years
Employment Type: Full-Time
Job Overview
We are seeking a highly skilled Agentic AI Engineer to design, develop, and deploy intelligent AI agents capable of autonomous decision-making and task execution. The ideal candidate will have hands-on experience with Large Language Models (LLMs), agent frameworks, vector databases, and cloud platforms.
You will play a key role in building scalable AI-driven systems by integrating LLMs with tools, APIs, and enterprise data sources.
Key Responsibilities
- Design and develop agentic AI architectures for autonomous workflows and decision-making
- Build and deploy LLM-powered applications and AI agents for real-world use cases
- Integrate multiple LLM providers (OpenAI, Anthropic, open-source models, etc.)
- Develop and maintain RAG (Retrieval-Augmented Generation) pipelines
- Implement solutions using frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen
- Work with vector databases like Pinecone, Weaviate, Milvus, Chroma, FAISS
- Design and implement multi-agent systems for complex problem-solving
- Deploy AI solutions on AWS, Azure, or GCP environments
- Build CI/CD pipelines, containerize applications using Docker, and manage deployments
- Monitor and optimize model performance, latency, and cost
- Collaborate with cross-functional teams (Data, ML, Product)
- Ensure security, governance, and responsible AI practices
Required Skills
- Strong programming experience in Python
- Hands-on experience with LLMs (OpenAI, Anthropic, Cohere, Mistral, Llama, etc.)
- Experience building agent-based AI systems
- Proficiency with frameworks like LangChain, LlamaIndex, CrewAI, AutoGen
- Experience with vector databases (Pinecone, Weaviate, Milvus, Chroma, FAISS)
- Strong understanding of RAG architecture
- Knowledge of prompt engineering and LLM optimization techniques
- Experience with cloud platforms (AWS / Azure / GCP)
- Familiarity with Docker, Kubernetes, CI/CD pipelines
- Strong understanding of APIs and microservices architecture
Good to Have
- Experience with fine-tuning / PEFT techniques (LoRA, adapters, etc.)
- Knowledge of LLMOps / MLOps tools
- Experience with LLM evaluation frameworks
- Familiarity with knowledge graphs and semantic search
- Experience building AI copilots, chatbots, or autonomous agents
- Understanding of data privacy, governance, and Responsible AI
