Agentic AI Developer

Key Responsibilities

• Agentic AI Development:
• Build, customize, and deploy AI agents using frameworks like LangChain, AutoGen, CrewAI, and Haystack.
• Enable agent reasoning, planning, and tool-use for complex tasks.
• RAG Pipeline Design:
• Implement and optimize RAG pipelines for enterprise-scale knowledge retrieval.
• Work with vector databases (Pinecone, FAISS, Weaviate, Milvus) to manage embeddings and context injection.
• Fine-tune retrieval strategies, chunking logic, and metadata tagging for high-quality responses.
• Prompt Engineering & LLM Integration:
• Develop structured prompts, context-aware query chains, and workflows for LLMs (OpenAI, Anthropic, Llama, Mistral, etc.).
• Integrate RAG-enabled LLMs into APIs, chatbots, and enterprise applications.
• Automation & Platform Development:
• Create orchestration pipelines for AI agents and RAG workflows.
• Contribute to building internal AI platforms, dashboards, and monitoring systems.
• Experimentation & Research:
• Stay current with new developments in RAG, multi-agent systems, and reasoning models.
• Rapidly prototype AI solutions to demonstrate value to business teams.
  

Required Skills

• Programming: Strong in Python; familiarity with JavaScript/TypeScript or Go is a plus.
• LLM Frameworks: Experience or coursework in LangChain, LlamaIndex, Haystack, or AutoGen.
• RAG Expertise: Understanding of RAG concepts, document indexing, embeddings, retrieval strategies, and vector DBs.
• Databases: PostgreSQL/MySQL for structured data; Pinecone, Weaviate, Milvus, FAISS for vectors.
• APIs & Cloud: Knowledge of REST/GraphQL APIs and cloud services (AWS/GCP/Azure).
• Version Control: Git, GitHub/GitLab, and CI/CD pipelines.
  

Preferred Skills (Good-to-Have)

• Familiarity with LangGraph and other agent orchestration libraries.
• Exposure to multi-agent collaboration patterns and reasoning models (OpenAI o1, DeepSeek-R1).
• Knowledge of document preprocessing, semantic search, and hybrid retrieval.
• Understanding of MLOps for deploying and monitoring AI pipelines.
• Experience with Docker, Kubernetes, and distributed systems.
  

Qualifications

• Bachelor’s degree in Computer Science, Engineering, AI/ML, or related field.
• 0–3 years of relevant experience (internships, hackathons, or open-source contributions preferred).
• Strong analytical skills, eagerness to experiment, and enthusiasm to learn cutting-edge AI tools.