Generative AI Engineer

Skill- Generative AI

Location- Hyderabad

Exp -2+yaers

JD

• Should have strong technical expertise in Python, hands-on experience with at least one GenAI framework (LangGraph, LangChain, or Google AI Development Kit), and strong working knowledge of one hyperscaler platform (Google Cloud, Azure, or AWS).
• The associate should lead solution design, integrating LLMs into enterprise workflows, mentoring team members, and driving production-grade implementation of GenAI use cases.
• Good knowledge of MLOps or DevOps teams to automate model deployment, versioning, and monitoring.

Key Responsibilities:

1. Solution Design

• Design, architect, and implement Generative AI workflows and agents using frameworks such as LangChain, LangGraph, or Google ADK.
• Integrate LLMs (e.g., Llama, Gemini, GPT, Claude) into enterprise systems and custom applications.
• Define and implement retrieval-augmented generation (RAG) pipelines using vector databases (e.g., ChromaDB, Pinecone, FAISS, Weaviate, Vertex AI Matching Engine).
• Architect scalable and secure GenAI microservices leveraging cloud-native components.

2. Development & Implementation

• Lead Python-based development efforts for building prompt orchestration, tool agents, and data pipelines.
• Develop and deploy APIs or microservices integrating LLMs with enterprise data sources.
• Implement prompt optimization, context management, and model performance tuning.

3. Cloud Integration

• Architect, deploy, and monitor GenAI worklo ads on one hyperscaler:
• GCP (Vertex AI, Document AI, AlloyDB, BigQuery, Cloud Run)
• Azure (OpenAI Service, Cognitive Search, Azure ML)
• AWS (Bedrock, SageMaker, Lambda, API Gateway)
• Manage cloud infrastructure for scaling AI models, ensuring cost efficiency and compliance.