Funic Tech - AI Engineer - Python

# AI Engineer / Data Scientist - GenAI & Agentic AI

• Experience : 5-7 Years
• Location : Bangalore
• Joining : Immediate to 15 Days
• Job Summary :

We are looking for a hands-on - AI Engineer / Data Scientist- with strong experience in - Generative AI, Agentic AI, LLMs, RAG, and Python- to design, develop, deploy, and optimize production-grade AI applications.

The ideal candidate should have practical experience building - agentic workflows, RAG-based solutions, enterprise AI applications, and LLM-powered automation- , along with strong exposure to the - Azure AI ecosystem, Databricks, MLflow, and Kubernetes- .

Key Responsibilities

• Design and develop production-ready - Generative AI and Agentic AI applications- for enterprise use cases.
• Build intelligent agent workflows using frameworks such as - LangGraph, AutoGen, CrewAI, Semantic Kernel, or PydanticAI .
• Develop advanced - RAG pipelines- including document ingestion, chunking, embeddings, vector search, hybrid search, re-ranking, memory, and tool orchestration.
• Integrate - LLMs- such as GPT, Claude, LLaMA, and other foundation models into enterprise applications.
• Develop - multi-agent and tool-using AI systems- capable of executing complex enterprise workflows.
• Implement prompt engineering techniques to improve accuracy, reliability, and performance.
• Develop AI solutions using - Azure OpenAI, Azure AI Search, and Azure AI Services- .
• Build and manage ML/AI workflows using - Databricks, MLflow, and Delta Lake- .
• Develop REST APIs, WebSockets, and event-driven services to integrate AI applications with enterprise systems.
• Deploy containerized AI workloads using - Docker and Kubernetes- , including - Azure AKS/ARO- .
• Implement CI/CD pipelines using - Jenkins, GitHub Actions, or similar tools- .
• Implement responsible AI practices, including - guardrails, content filtering, prompt-injection protection, and secure AI interactions- .
• Develop evaluation frameworks and metrics to measure - task success, response quality, hallucination, latency, reliability, and cost- .
• Monitor and troubleshoot AI applications in production environments.
• Optimize LLM applications for scalability, performance, latency, reliability, and cost.
• Collaborate with Data Scientists, ML Engineers, Cloud Engineers, DevOps teams, and business stakeholders.
• Follow - SDLC, Agile, version control, testing, documentation, and production deployment