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