
Agentic AI Developer
19 hours ago
Designation - Agentic AI Developer
Work Location - Hyderabad (Hybrid)
Experience - 0 to 2 years
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
- Bachelors degree in Computer Science, Engineering, AI/ML, or related field.
- 03 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.
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