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 Operations Engineer will handle end-to-end processes from data analysis and feature engineering to model deployment and monitoring, and will demand a proactive and collaborative mindset, working closely with process owners, business stakeholders and engineering teams to deliver scalable, production-ready AI and automation solutions. The candidate will take ownership of the entire model lifecycle driving experimentation, validation, deployment, and continuous optimization to create high-impact, AI-powered business value.
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)
- ML Model Development: Strong grasp of statistical modelling, supervised/unsupervised learning, time-series forecasting, and NLP.
- Data Engineering: Experience with ETL/ELT pipelines, data ingestion, transformation, and orchestration
- 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
Preferred Qualifications:
- Knowledge of natural language processing (NLP) and custom/computer,
- Familiarity with marketing analytics, attribution modelling
- 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, Graph ML) is added advantage
- Certifications in AI/ML
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.
- Utilize Azure AI services and infrastructure for development, training, inferencing, and model lifecycle management.
- 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 hyperparameter tuning, cross-validation, and performance evaluation using industry-standard metrics to ensure model robustness, relevance, and accuracy.
- Integrate models and services into business applications through RESTful APIs.
- 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.
- Support and collaborate on the integration of large language models (LLMs), embeddings, vector databases, and RAG techniques where applicable.
- Collaborate with cross-functional teams including software engineers, product managers, process owners, 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.
- 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)
- ML Model Development: Strong grasp of statistical modelling, supervised/unsupervised learning, time-series forecasting, and NLP.
- Data Engineering: Experience with ETL/ELT pipelines, data ingestion, transformation, and orchestration
- 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
