Senior Data Scientist

**Job Description:**We are seeking a skilled Machine Learning Engineer to join our team. The ideal candidate will have 3-5 years of experience in designing, building, deploying, and optimizing ML/AI solutions across various business problems. They will be responsible for the full ML lifecycle, from data pre-processing and experimentation to deployment, monitoring, and continuous improvement.Key Responsibilities:Build, train, and evaluate machine learning models (classification, regression, clustering, NLP, deep learning).Develop custom models or adapt pre-trained models (transfer learning).Conduct feature engineering, feature selection, and data transformation.Perform hyperparameter tuning using tools like Optuna, Ray Tune, or built-in frameworks.Evaluate model performance using appropriate metrics (AUC, F1, MAE, BLEU, WER, etc.).Data Engineering & Processing:Work with structured and unstructured data (text, images, audio, logs, documents).Build data pipelines using Python, SQL, Spark, Airflow, or similar.Apply data quality checks, deduplication, anomaly detection, and data validation (Great Expectations, pydantic).ML Deployment & MLOps:Deploy models as APIs using FastAPI, Flask, Node, or cloud-native tools.Implement CI/CD pipelines for ML (GitHub Actions, GitLab, Jenkins).Use containerization & orchestration (Docker, Kubernetes).Manage models using MLflow, Vertex AI, SageMaker, Azure ML, or Databricks.Implement automated retraining, model versioning, rollback, and monitoring.Handle inference optimizations (quantization, batching, caching).LLM & GenAI (Preferred but not mandatory):Work with foundation models like GPT, LLaMA, Claude, Mistral, etc.Build RAG pipelines with vector databases (Qdrant, Pinecone, FAISS, Elasticsearch).Prompt engineering and evaluation.Optimize embeddings, context windows, and chunking strategies.Integrate LLMs into production APIs and workflows.Cloud & Infrastructure:Experience with at least one cloud provider (AWS, Azure, GCP).Use managed ML services like: AWS: SageMaker, Lambda, Textract, Comprehend Azure: AI Foundry, Functions, Search GCP: Vertex AI, Cloud Run.Manage GPU/CPU compute workloads efficiently.Software Engineering Best Practices:Write clean, modular, well-tested Python code.Maintain repositories adhering to branching, versioning, and code review standards.Use design patterns for scalable ML pipelines.Build internal tools for automation, testing, and monitoring.Cross-functional Collaboration:Work closely with product managers, data engineers, backend engineers, and business analysts.Translate business problems into ML solutions with measurable ROI.Document technical designs, APIs, models, architecture diagrams.Monitoring, Observability & Optimization:Implement model monitoring for data drift, concept drift, performance degradation.Evaluate model reliability, bias, fairness.Implement logging & metrics (Prometheus, Grafana, ELK).Continuously improve model performance and resource usage.Required Skills & Experience:Technical Skills:Strong Python skills (NumPy, Pandas, Scikit-learn, PyTorch or TensorFlow).Proficiency in ML algorithms, data structures, and ML system design.Experience with deep learning, especially CNN/RNN/Transformers.Knowledge of LLMs, embeddings, tokenizer concepts (preferred).Strong understanding of vector databases (Qdrant, FAISS, Pinecone).Hands-on experience with REST APIs, microservices, and cloud-native deployment.Familiarity with SQL, NoSQL, and data warehouses (Snowflake/BigQuery/Redshift).MLOps & Infrastructure:Docker, Kubernetes, Terraform (good to have).MLflow or equivalent for experiment tracking.CI/CD automation experience.GPU optimization and parallel processing experience.Soft Skills:Strong problem-solving abilities.Excellent communication for explaining technical concepts to non-tech stakeholders.Ownership mindset and ability to work independently.Ability to work in a fast-paced, iterative, PoC-driven environment.Curiosity to continuously learn new technologies.Preferred Qualifications:Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or equivalent experience.Experience building ML products end-to-end.Experience in domains like FinTech, Healthcare, Retail, Manufacturing, or Real Estate is a plus.Contributions to open-source ML frameworks are a bonus.Torinit's Ethos:Transparent: We commit to ensuring information flows freely, enabling everyone to contribute effectively and make well-informed choices.Resourceful: We encourage creative thinking and efficient use of resources.United: We strive to create win-win scenarios that benefit our clients, our team, and our company.Excellence: We constantly raise the bar and ask ourselves,