AI Agent Factory Developers

Title: AI Agent Factory Developers

Location: Pan India

Experience: 3-8 years

Must-Haves:Python , AI Agent / Multi Agent / RAG / Hugging Face OR LangChain OR OpenAI API OR Transformers

Key Responsibilities

LLM Integration Development:Build and fine-tune LLMs for task-specific applications using techniques like prompt engineering, retrieval-augmented generation (RAG), fine-tuning, and model adaptation.

AI Agent Engineering

Design, develop, and orchestrate AI agents capable of reasoning, planning, tool use (e.g., APIs, plugins), and autonomous execution for user-defined goals.

GenAI Use Case Implementation

Deliver GenAI-powered solutions such as chatbots, summarizers, document QA systems, assistants, and co-pilot tools using frameworks like LangChain or LlamaIndex.

System Integration

Connect LLM-based agents to external tools, APIs, databases, and knowledge sources for real-time, contextualized task execution.

Performance Tuning

Optimize model performance, cost-efficiency, safety, and latency using caching, batching, evaluation tools, and monitoring systems.

Collaboration Documentation

Work closely with AI researchers, product teams, and engineers to iterate quickly. Maintain well-structured, reusable, and documented codebases.

Required Qualifications

• 35 years of experience in AI/ML, with at least 12 years hands-on with GenAI or LLMs.
• Strong Python development skills and experience with ML frameworks (e.g., Hugging Face, LangChain, OpenAI API, Transformers).
• Familiarity with LLM orchestration, vector databases (e.g., FAISS, Pinecone, Weaviate), and embedding models.
• Understanding of prompt engineering, agent architectures, and conversational AI flows.
• Bachelors or Masters degree in Computer Science, Artificial Intelligence, or related field.
  

Preferred Qualifications

• Experience deploying AI systems in cloud environments (AWS/GCP/Azure) or with containerized setups (Docker/Kubernetes).
• Familiarity with open-source LLMs (LLaMA, Mistral, Mixtral, etc.) and open-weight tuning methods (LoRA, QLoRA).
• Exposure to RAG pipelines, autonomous agents (e.g., Auto-GPT, BabyAGI), and multi-agent systems.
• Knowledge of model safety, evaluation, and compliance standards in GenAI."""
  

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