Key Responsibilities

Design, develop, and deploy machine learning and AI models

Build and maintain data pipelines for training and inference

Work on supervised and unsupervised learning algorithms

Implement NLP, Computer Vision, or Deep Learning solutions

Optimize models for performance, scalability, and accuracy

Collaborate with data engineers, analysts, and product teams

Deploy models using cloud platforms (AWS/Azure/GCP)

Monitor model performance and perform continuous improvements

Conduct data preprocessing, feature engineering, and model evaluation

Document models, processes, and experiments

Required Skills & Qualifications

Technical Skills

Strong proficiency in Python

Experience with Machine Learning libraries:

Scikit-learn

TensorFlow / PyTorch

Knowledge of data manipulation tools (Pandas, NumPy)

Experience with ML algorithms (regression, classification, clustering)

Familiarity with Deep Learning frameworks

Knowledge of NLP or Computer Vision techniques

Experience in model deployment (APIs, microservices)

Strong understanding of statistics and probability

• Primary skills:Domain->Turbomachinery->Steam Turbine->Rotor

Preferred Skills (Nice-to-Have)

Experience with Generative AI / LLMs (GPT, LangChain, Hugging Face)

Knowledge of MLOps tools (MLflow, Kubeflow, Airflow)

Experience with Docker and Kubernetes

Familiarity with Big Data tools (Spark, PySpark)

Experience with REST APIs and microservices architecture

Knowledge of cloud AI services (AWS SageMaker, Azure ML, GCP AI)

Understanding of vector databases and embeddings (FAISS, Pinecone)

Exposure to CI/CD pipelines and DevOps practices