AI Engineer – Generative AI & Agentic AI
📍 Location: Bangalore
💼 Experience: 4–6 Years
📌 Employment: Full-Time
🔹 Role Overview
We are looking for a highly skilled AI Engineer with strong hands-on experience in Generative AI, LLMs, Agentic AI, RAG, Python, and Azure AI technologies.
The ideal candidate should be capable of designing, developing, optimizing, and deploying production-ready AI applications using modern AI frameworks and Azure cloud technologies.
🔹 Key Responsibilities
- Design and develop scalable Generative AI and Agentic AI solutions using LLMs.
- Build AI applications using OpenAI APIs, AutoGen, RAG, and MCP.
- Develop intelligent agents and multi-agent workflows for enterprise use cases.
- Design and implement RAG pipelines, including data ingestion, retrieval, ranking, and response generation.
- Work with structured, unstructured, and graph-based data.
- Integrate AI solutions with Azure AI Services, Azure OpenAI, Azure Cosmos DB, and Graph capabilities.
- Develop high-quality, scalable applications using Python.
- Manage the complete lifecycle from design and development to testing, deployment, monitoring, and production support.
- Optimize AI applications for latency, throughput, scalability, reliability, and resource utilization.
- Troubleshoot production issues and continuously improve application performance.
- Collaborate with engineering, product, and cloud teams to deliver production-ready AI solutions.
🔹 Key Skills
Generative AI: LLMs, OpenAI, RAG, Agentic AI, AutoGen, MCP, Prompt Engineering
Programming: Python, REST APIs, Microservices, Software Development
Azure: Azure OpenAI, Azure AI Services, Azure Cosmos DB, Azure Cloud
AI/ML: Embeddings, Transformers, Vector Search, Neural Networks, Model Inference
Databases: Cosmos DB, Graph DB, Vector Databases, Similarity Search
Production: Deployment, Performance Optimization, Scalability, Monitoring, Troubleshooting
Good to Have: Docker, Kubernetes, CI/CD, Azure AI Search, Pinecone, Milvus, Weaviate
🔹 Production & Optimization
Candidates should have practical experience taking AI applications from development to production, with knowledge of:
- Application and API deployment
- Latency and performance optimization
- Compute and memory optimization
- Scalability and concurrency
- Resource and cost optimization
- Monitoring, logging, and debugging
- Production troubleshooting
- Reliable and maintainable AI services
🔹 Good to Have
- Experience with Docker and Kubernetes
- CI/CD and DevOps practices
- REST APIs and Microservices
- Azure Functions, AKS, or App Services
- AI/ML model evaluation and observability
- Experience with Azure AI Search, Pinecone, Milvus, Weaviate, or similar
- Experience building enterprise AI assistants, copilots, or intelligent agents