Sr. Machine Learning Engineer

Job Summary:We are looking for a versatile and results-driven Data Scientist / Machine Learning Developer with 7+ years of experience to join our dynamic team. The ideal candidate will have a strong background in both data science and machine learning, capable of handling end-to-end processes from data analysis and feature engineering to model deployment and monitoring. This role demands a proactive and collaborative mindset, working closely with product owners and engineering teams to deliver scalable, production-ready ML solutions. You will take ownership of the entire model lifecycle driving experimentation, validation, deployment, and continuous optimization to create high-impact, AI-powered business value.In this Role, Your Responsibilities Will Be: Develop, train and deploy machine learning, deep learning AI models for a variety of business use cases such as classification, prediction, recommendation, NLP and Image Processing. Design and implement end-to-end ML workflows from data ingestion and preprocessing to model deployment and monitoring. Collect, clean, and preprocess structured and unstructured data from multiple sources using industry-standard techniques such as normalization, feature engineering, dimensionality reduction, and optimization. Perform exploratory data analysis (EDA) to identify patterns, correlations, and actionable insights. Apply advanced knowledge of machine learning algorithms including regression, classification, clustering, decision trees, ensemble methods, and neural networks. Use Azure ML Studio, TensorFlow, PyTorch, and other ML frameworks to implement and optimize model architectures. Perform hyperparameter tuning, cross-validation, and performance evaluation using industry-standard metrics to ensure model robustness and accuracy. Integrate models and services into business applications through RESTful APIs developed using FastAPI, Flask or Django. Build and maintain scalable and reusable ML components and pipelines using Azure ML Studio, Kubeflow, and MLflow. Enforce and integrate AI guardrails: bias mitigation, security practices, explainability, compliance with ethical and regulatory standards. Deploy models in production using Docker and Kubernetes, ensuring scalability, high availability, and fault tolerance. Utilize Azure AI services and infrastructure for development, training, inferencing, and model lifecycle management. Support and collaborate on the integration of large language models (LLMs), embeddings, vector databases, and RAG techniques where applicable. Monitor deployed models for drift, performance degradation, and data quality issues, and implement retraining workflows as needed. Collaborate with cross-functional teams including software engineers, product managers, business analysts, and architects to define and deliver AI-driven solutions. Communicate complex ML concepts, model outputs, and technical findings clearly to both technical and non-technical stakeholders. Stay current with the latest research, trends, and advancements in AI/ML and evaluate new tools and frameworks for potential adoption. Maintain comprehensive documentation of data pipelines, model architectures, training configurations, deployment steps, and experiment results. Drive innovation through experimentation, rapid prototyping, and the development of future-ready AI components and best practices. Write modular, maintainable, and production-ready code in Python with proper documentation and version control. Contribute to building reusable components and ML accelerators. Qualifications: Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or a related field over 7+ years. Proven experience as a Data Scientist, ML Developer, or in a similar role. Strong command of Python and ML libraries (e.g., Azure ML Studio, scikit-learn, TensorFlow, PyTorch, XGBoost). Data Engineering: Experience with ETL/ELT pipelines, data ingestion, transformation, and orchestration (Airflow, Dataflow, Composer). ML Model Development: Strong grasp of statistical modelling, supervised/unsupervised learning, time-series forecasting, and NLP. Proficiency in Python Strong knowledge of machine learning algorithms, frameworks (e.g., TensorFlow, PyTorch, scikit-learn), and statistical analysis techniques. Proficiency in programming languages such as Python, R, or SQL. Experience with data preprocessing, feature engineering, and model evaluation techniques. MLOps & Deployment: Hands-on experience with CI/CD pipelines, model monitoring, and version control. Familiarity with cloud platforms (e.g., Azure (Primarily), AWS and deployment tools. Knowledge of DevOps platform. Excellent problem-solving skills and attention to detail. Strong communication and collaboration skills, with the ability to work effectively in a team environment. Preferred Qualifications: Proficiency in Python, with libraries like pandas, NumPy, scikit-learn, spacy, NLTK and Tensor Flow, Pytorch Knowledge of natural language processing (NLP) and custom/computer, YoLo vision techniques. Experience with Graph ML, reinforcement learning, or causal inference modeling. Familiarity with marketing analytics, attribution modelling, and A/B testing methodologies. Working knowledge of BI tools for integrating ML insights into dashboards. Hands on MLOps experience, with an appreciation of the end-to-end CI/CD process Familiarity with DevOps practices and CI/CD pipelines. Experience with big data technologies (e.g., Hadoop, Spark) is added advantage Certifications in AI/ML