Machine Learning Engineer

Job Summary If you are a professional looking for an opportunity to work with the global Emerson Systems and Software organization, this is a stimulating opportunity for you The Machine Learning Engineer should 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: Developing, training and deploying machine learning, deep learning AI models for a variety of business use cases such as classification, prediction, recommendation, NLP and Image Processing. Designing and implementing end-to-end ML workflows from data ingestion and preprocessing to model deployment and monitoring. Collecting, cleaning, and preprocessing structured and unstructured data from multiple sources using industry-standard techniques such as normalization, feature engineering, dimensionality reduction, and optimization. Performing exploratory data analysis (EDA) to identify patterns, correlations, and actionable insights. Applying advanced knowledge of machine learning algorithms including regression, classification, clustering, decision trees, ensemble methods, and neural networks. Using Azure ML Studio, TensorFlow, PyTorch, and other ML frameworks to implement and optimize model architectures. Performing hyperparameter tuning, cross-validation, and performance evaluation using industry-standard metrics to ensure model robustness and accuracy. Integrating models and services into business applications through RESTful APIs developed using FastAPI, Flask or Django. Building and maintaining scalable and reusable ML components and pipelines using Azure ML Studio, Kubeflow, and MLflow. Enforcing and integrating AI guardrails: bias mitigation, security practices, explainability, compliance with ethical and regulatory standards. Deploying models in production using Docker and Kubernetes, ensuring scalability, high availability, and fault tolerance. Utilizing Azure AI services and infrastructure for development, training, inferencing, and model lifecycle management. Supporting and collaborate on the integration of large language models (LLMs), embeddings, vector databases, and RAG techniques where applicable. Monitoring deployed models for drift, performance degradation, and data quality issues, and implement retraining workflows as needed. Collaborating with cross-functional teams including software engineers, product managers, business analysts, and architects to define and deliver AI-driven solutions. Communicating complex ML concepts, model outputs, and technical findings clearly to both technical and non-technical stakeholders. Staying current with the latest research, trends, and advancements in AI/ML and evaluate new tools and frameworks for potential adoption. Maintaining comprehensive documentation of data pipelines, model architectures, training configurations, deployment steps, and experiment results. Driving innovation through experimentation, rapid prototyping, and the development of future-ready AI components and best practices. Writing modular, maintainable, and production-ready code in Python with proper documentation and version control. Contributing to building reusable components and ML accelerators. Who You Are: You encourage diverse thinking to promote and nurture innovation. You readily take action on challenges, without unnecessary planning. You have a strong bottom-line orientation. For this Role You Will Need: 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. Our Culture & Commitment to You At Emerson, we prioritize a workplace where every employee is valued, respected, and empowered to grow. We foster an environment that encourages innovation, collaboration, and diverse perspectives—because we know that great ideas come from great teams. Our commitment to ongoing career development and growing an inclusive culture ensures you have the support to thrive. Whether through mentorship, training, or leadership opportunities, we invest in your success so you can make a lasting impact. We believe diverse teams, working together are key to driving growth and delivering business results. We recognize the importance of employee wellbeing. We prioritize providing competitive benefits plans, a variety of medical insurance plans, Employee Assistance Program, employee resource groups, recognition, and much more. Our culture offers flexible time off plans, including paid parental leave (maternal and paternal), vacation and holiday leave. #J-18808-Ljbffr