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

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

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