Vacancy description
FUNIC TECH PRIVATE LIMITED
India, Bengaluru
Funic Tech - AI Engineer - Python in Bengaluru, India is listed on Jobeax. Browse 30,000+ vacancies available.
# 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 https://jobeax.com/link/uTkIfl1tAHKRWQPa 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 (ref:hirist.tech)
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