We are seeking an creative Generative AI Engineer with strong expertise in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Python, and Machine Learning. The ideal candidate will be responsible for designing, developing, and deploying AI-powered applications that leverage state-of-the-art language models to solve complex business problems.

The candidate should have hands-on experience with LLM integration, prompt engineering, vector databases, RAG pipelines, machine learning model development, and cloud-based AI services. This role requires close collaboration with data scientists, software engineers, product teams, and business stakeholders to build scalable AI solutions.

Key ResponsibilitiesGenerative AI Development

- Design, develop, and deploy Generative AI solutions using modern LLM frameworks.
- Build AI-powered applications such as intelligent chatbots, virtual assistants, document processing systems, and content generation platforms.
- Integrate foundation models through APIs and open-source frameworks.
- Optimize AI applications for scalability, performance, and cost efficiency.
- Stay updated with the latest advancements in Generative AI technologies.

Large Language Models (LLMs)
- Develop applications utilizing commercial and open-source LLMs.
- Fine-tune, evaluate, and optimize language models where applicable.
- Design effective prompt engineering strategies for improved model responses.
- Implement model monitoring, evaluation, and response quality metrics.
- Address AI safety, hallucination reduction, and responsible AI practices.

Retrieval-Augmented Generation (RAG)
- Design and implement Retrieval-Augmented Generation (RAG) pipelines.
- Build document ingestion, embedding, indexing, and retrieval workflows.
- Integrate vector databases for semantic search and knowledge retrieval.
- Optimize retrieval accuracy,



context management, and response relevance.
- Work with structured and unstructured enterprise data sources.

Machine Learning & Data Science
- Develop, train, evaluate, and deploy machine learning models.
- Perform data preprocessing, feature engineering, and model validation.
- Implement NLP and text analytics solutions.
- Analyze model performance and improve prediction accuracy.
- Collaborate with data engineering teams to build scalable ML pipelines.

Python Development
- Develop scalable backend services and AI applications using Python.
- Build REST APIs for AI model integration.
- Write clean, modular, and maintainable production-grade code.
- Develop reusable libraries and automation scripts.
- Integrate AI solutions with enterprise applications.

Deployment & MLOps
- Deploy AI models to cloud environments and production systems.
- Implement CI/CD pipelines for machine learning workflows.
- Monitor model performance, usage, and reliability.
- Support containerization using Docker and orchestration with Kubernetes (preferred).
- Maintain model versioning and experiment tracking.

Collaboration & Innovation
- Collaborate with product managers, engineers, and business stakeholders.
- Participate in architecture discussions and AI solution design.
- Conduct proof-of-concepts (POCs) for emerging AI technologies.
- Mentor junior developers and promote AI best practices.

Required SkillsGenerative AI
- Generative AI
- Large Language Models (LLMs)
- Prompt Engineering




- AI Agents
- Responsible AI

RAG & Knowledge Retrieval
- Retrieval-Augmented Generation (RAG)
- Vector Databases
- Semantic Search
- Embeddings
- Document Processing

Machine Learning
- Machine Learning
- Natural Language Processing (NLP)
- Deep Learning
- Model Training & Evaluation
- Feature Engineering

Programming
- Python
- FastAPI / Flask
- REST APIs
- Object-Oriented Programming

AI Frameworks
- LangChain
- LlamaIndex
- Hugging Face Transformers
- OpenAI APIs
- PyTorch
- TensorFlow
- Scikit-learn

Databases
- PostgreSQL
- MongoDB
- Redis
- Vector Databases (Pinecone, ChromaDB, FAISS, Weaviate, or Milvus)

Cloud & DevOps
- Microsoft Azure / AWS / Google Cloud Platform (GCP)
- Docker
- Kubernetes
- Git
- CI/CD Pipelines

Preferred Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
- 4–8 years of experience in Python development and Machine Learning.
- Hands-on experience building Generative AI and RAG-based applications.
- Strong understanding of NLP, embeddings, vector search, and LLM architectures.
- Experience deploying AI applications in cloud environments.
- Familiarity with MLOps practices, model monitoring, and experiment tracking.
- Excellent analytical, problem-solving, and communication skills.
- Experience working in Agile/Scrum development environments.

Preferred Certifications
- Microsoft Certified: Azure AI Engineer Associate (AI-102)
- Microsoft Certified: Azure Data Scientist Associate (DP-100)
- AWS Certified Machine Learning – Specialty
- Google Professional Machine Learning Engineer
- TensorFlow Developer Certificate (Preferred)
- Databricks Certified Machine Learning Professional (Preferred)

Work Location: Hybrid remote in Noida, Uttar Pradesh (Noida)

📌 Generative AI Engineer (LLMs | RAG | Python | Machine Learning) (Noida)
🏢 UIDM
📍 Noida