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AI Engineer

Work Location: Hyderabad (Hybrid Model)

Prestige Skytech, Financial District

Experience: 5 - 8 years

Mandatory Skills: AI, ML, RAG, LLM

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.
Strong analytical skills, eagerness to experiment, and enthusiasm to learn cutting-edge AI tools.

Skills: llm,ai,rag,agents,ml

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