Description

You will design, build, and deploy end-to-end machine learning and generative AI models that drive product outcomes.

This role is on-site.

Responsibilities

• Design and implement ML pipelines, including data ingestion, preprocessing, and monitoring.
• Apply advanced deep learning techniques to solve complex problems in NLP, computer vision, and LLM fine-tuning.
• Build custom generative AI applications using LangChain and Retrieval-Augmented Generation (RAG).
• Develop reusable components for feature engineering, training, and deployment.
• Own the experimentation lifecycle, including hypothesis formulation, validation, and A/B testing.

Required Skills

• 8+ years of experience in AI/ML/Data Science with production-level deployments.
• Strong hands-on experience with Python, TensorFlow, PyTorch, SciKit-learn, and Hugging Face Transformers.
• Deep knowledge of machine learning algorithms, neural networks, LLMs, and generative AI models.
• Expertise in building and optimizing ML pipelines using Airflow, MLflow, or Kubeflow.
• Experience with cloud services such as AWS SageMaker.
• Solid understanding of data structures, algorithms, and software engineering principles.
• Working knowledge of SQL and NoSQL databases, and data handling frameworks like Pandas or Spark.
• Experience with MLOps and CI/CD pipelines for production model monitoring.

Preferred Skills

• Any Graduate

Key Skills

Education

Any Graduate