Machine Learning Engineer

Job Description Position Summary: The Machine Learning Engineer is responsible for designing, building, deploying, and optimizing machine learning models and data-driven solutions that support business objectives. This role bridges software engineering and data science, ensuring ML models are scalable, efficient, production-ready, and integrated seamlessly into applications and systems. The ML Engineer collaborates with data scientists, software developers, product managers, and domain experts to turn analytical prototypes into robust, high-performing ML pipelines. A Snapshot of your Day How You'll Make An Impact (responsibilities Of Role) Model Development & Optimization - Develop, implement, and optimize machine learning models for classification, regression, forecasting, and other analytics tasks. - Collaborate with data scientists to refine features, algorithms, and model architecture. - Evaluate model performance using appropriate metrics and ensure continuous improvement. - ML Pipeline Engineering - Build scalable, automated ML pipelines (data preprocessing, training, validation, deployment). - Implement MLOps best practices, including CI/CD for ML, versioning, monitoring, and retraining workflows. - Use modern ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or XGBoost. - Data Engineering & Preparation - Work with large, structured and unstructured datasets. - Build data processing workflows using Python, SQL, and big data tools. - Ensure data quality, consistency, and proper feature engineering. - Deployment & Productionization - Deploy machine learning models to cloud or on-premise environments (AWS, Azure, GCP). - Develop REST APIs, microservices, or batch processes to expose ML capabilities. - Monitor model performance in production and address drift, degradation, or bias issues. - Collaboration & Documentation - Work closely with software engineers to integrate ML systems into applications. - Partner with data scientists to validate model assumptions and ensure reproducibility. - Document ML workflows, architecture, and operational procedures. - Research & Innovation - Stay updated with new ML techniques, tools, and best practices. - Evaluate new technologies (e.g., LLMs, transformers, AutoML, graph ML). - Experiment with advanced models and help drive innovation within the organization. What You Bring (required Qualification And Skill Sets) - Bachelor's or Master's degree in Computer Science, AI, Data Science, Engineering, or related field. - 25+ years of experience developing and deploying machine learning solutions. - Strong experience with Python and ML libraries (TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy). - Experience with cloud ML services (AWS SageMaker, Azure ML, GCP Vertex AI). - Solid understanding of mathematics and statistics related to ML (optimization, probability, linear algebra). - Knowledge of data engineering tools (Spark, Kafka, Airflow, Databricks) is a plus. - Experience building APIs and production services (FastAPI, Flask, Django). - Strong problem-solving and analytical thinking. - Curiosity about new technologies and a growth mindset. - Ability to balance experimentation with scalability and reliability. - Effective communication and teamwork skills. - Attention to detail and strong ownership of deliverables. Preferred Qualifications - Experience with deep learning, NLP, computer vision, or time-series forecasting. - Familiarity with MLOps platforms (MLflow, Kubeflow, DVC, BentoML). - Knowledge of containerization and orchestration (Docker, Kubernetes). - Background in distributed computing or big data processing. - Experience applying ML in domain-specific environments (IoT analytics, finance, geospatial, or industrial systems).