GenAI / AI-ML Engineer

Location : Gurugram (Hybrid)

Employment Type : Full-Time

Experience : 4 to 6 Years

Preference : Noida/NCR Local Candidates Only

Key Responsibilities :

- Design, develop, and deploy scalable AI/ML and Generative AI solutions.

- Build and optimize RAG (Retrieval-Augmented Generation) pipelines using modern frameworks and vector databases.

- Develop LLM-powered applications leveraging prompt engineering, AI agents, and multi-agent workflows.

- Fine-tune, evaluate, and monitor machine learning and deep learning models.

- Build REST APIs and backend services for AI applications.

- Design data preprocessing, feature engineering, and model evaluation pipelines.

- Integrate structured and unstructured data sources to deliver contextual AI solutions.

- Collaborate with cross-functional teams including Data Engineering, DevOps, and Product teams.

- Ensure scalability, reliability, and performance of AI applications in production environments.

Programming & Development :

- Strong hands-on experience in Python and SQL.

- Experience building APIs using FastAPI or similar frameworks.

- Strong understanding of software engineering best practices and Agile methodologies.

Machine Learning & Deep Learning :

- Hands-on experience with: Scikit-learn, TensorFlow, PyTorch, Keras.

- Strong understanding of: Regression, Classification, Clustering, Feature Engineering, Model Evaluation, Hyperparameter Tuning.

NLP & Generative AI :

- Minimum 1+ year of hands-on experience in GenAI/LLM projects.

- Experience with: Large Language Models (LLMs), Prompt Engineering, RAG Architectures, Agentic AI / Multi-Agent Systems, LangChain, LangGraph, OpenAI APIs, Hugging Face, LangSmith.

RAG & Vector Databases :

- Experience working with: Pinecone, FAISS.

- Knowledge of embedding models and semantic search architectures.

- Experience with RAG evaluation metrics such as RAGAS, BLEU, and ROUGE.

Cloud & DevOps :

- Hands-on experience with AWS services: EC2, S3, SageMaker, Bedrock.

- Experience with Docker, Git, JIRA, and CI/CD practices.