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EduRun Group

Generative AI Engineer-Python & RAG

Hyderabad, Telangana
via TheirStack5–15 yrs

Posted today · First seen Oct 26 · seen live today · via TheirStack

Skills mentioned

pythonjavaflaskfastapisqlrestawsazuregcpdockerkubernetessparkpytorchmachine learning

The posting, as published

Role: AI/ML Engineer Location: Bengaluru / Hyderabad / Chennai / Pune Experience: 5–15 Years Employment Type: Full-Time Notice Period: Immediate to 30 Days We are looking for experienced AI/ML Engineers with strong expertise in Generative AI, Python, Machine Learning, and RAG to develop and deploy scalable, production-ready AI applications. Mandatory Skills: • Strong hands-on experience in Python programming and Python coding • Expertise in Generative AI (Gen AI), Machine Learning, and RAG • Experience building LLM-powered applications, chatbots, summarization, and knowledge search solutions • Hands-on experience with OpenAI APIs, Azure AI, or open-source LLMs • Experience with embeddings, vector search, and retrieval optimization • Knowledge of vector databases such as FAISS, Pinecone, or pgvector • Experience with PyTorch, SQL, FastAPI, Flask, or Streamlit • Strong understanding of NLP/NLU, transformers, semantic search, and prompt engineering • Experience with REST APIs, Docker, and modern application architectures Key Responsibilities: 🔹 Develop and enhance Generative AI-powered applications 🔹 Design and implement RAG pipelines, including data preprocessing, embeddings, and retrieval 🔹 Integrate LLMs and AI services into enterprise applications 🔹 Build AI agents, agentic workflows, and intelligent automation solutions 🔹 Improve AI model accuracy, reliability, performance, and cost efficiency 🔹 Implement prompt engineering, evaluation techniques, and AI guardrails 🔹 Collaborate with product, data, and engineering teams 🔹 Support production deployment, scalability, and optimization of AI solutions Good to Have: • Experience with React.js or Java backend development • Knowledge of cloud platforms such as Azure, AWS, or GCP • Experience with MLOps, CI/CD, Kubernetes, and model monitoring • Exposure to distributed ML, Spark, multimodal AI, computer vision, or speech recognition • Knowledge of AI governance, security, and responsible AI practices

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