Description

You will lead the design and implementation of machine learning and generative AI models that drive product outcomes.

Responsibilities

• Design, build, and deploy end-to-end AI/ML pipelines, from data ingestion through monitoring.
• Apply advanced ML and deep learning techniques to solve complex problems in NLP, vision, and LLM fine-tuning.
• Work on custom generative AI applications using frameworks like LangChain and Retrieval-Augmented Generation (RAG).
• Build reusable components for data preprocessing, feature engineering, training, and deployment.
• Own the experimentation process, 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.

Key Skills

Education

Any Graduate