Overview

As a Machine Learning Engineer at Koch Capabilities India, you will ensure production machine learning solutions remain reliable, scalable, and trusted by the business teams who depend on them every day.

Responsibilities

• You will be part of the Data & Analytics capability supporting INVISTA and other Koch companies through Koch Capabilities India. Our team partners with business and technology stakeholders to build, deploy, and support data-driven solutions that improve decision-making and create long-term value. We combine analytics, engineering, and domain expertise to deliver reliable and scalable AI and machine learning capabilities across global operations.
• Monitor production data pipelines and resolve issues before they impact downstream models or business users.
• Maintain the reliability, performance, and availability of machine learning solutions in production environments.
• Identify model drift, data quality issues, and performance degradation, and implement corrective actions.
• Troubleshoot production incidents including failed pipelines, model retraining failures, stale datasets, and API or endpoint issues.
• Partner with stakeholders to understand business problems, evaluate available data, and define solution requirements.
• Assess whether business challenges are best addressed through machine learning, analytics, automation, reporting, or process improvements.
• Prepare and transform data, engineer features, and validate outputs to support advanced analytical and machine learning solutions.
• Deploy, monitor, and manage machine learning models using modern MLOps practices.
• Integrate model outputs into business processes and applications to drive adoption and measurable business outcomes.
• Contribute to the development of foundational machine learning solutions including regression, classification, clustering, anomaly detection, and forecasting models.
• Collaborate with cross-functional teams to continuously improve data, analytics, and AI capabilities.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Statistics, Mathematics, or a related technical field.
• 3+ years of experience deploying and supporting machine learning models in production environments.
• Experience deploying and supporting machine learning models in production environments.
• Strong Python programming skills with experience using libraries such as pandas, NumPy, scikit-learn, and visualization frameworks.
• Experience with machine learning techniques such as classification, regression, anomaly detection, time-series forecasting, natural language processing, or computer vision.
• Understanding of feature engineering, model evaluation, validation methodologies, and hyperparameter tuning.
• Strong SQL skills for data extraction, transformation, and analysis.
• Experience building and maintaining automated data pipelines.
• Knowledge of containerization, CI/CD practices, and model lifecycle management.
• Experience deploying solutions on cloud platforms such as AWS, Azure, or GCP.
• Strong problem-solving skills and ability to communicate effectively with technical and non-technical stakeholders.
• Demonstrated ownership mindset with the ability to proactively identify, communicate, and resolve issues.
• Experience with Snowflake, including Snowpark ML, Tasks, Streams, Dynamic Tables, Stored Procedures, or UDTFs.
• Experience with Kubernetes, AWS Lambda, Step Functions, GitLab CI, ArgoCD, JFrog, or similar modern platform technologies.
• Familiarity with Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or deep learning frameworks such as PyTorch.
• Experience with model explainability and interpretability techniques such as SHAP.
• Knowledge of manufacturing, chemical processes, supply chain operations, or industrial data systems such as OSI PI or SAP.
• Experience building analytical applications and visualizations using Streamlit, Power BI, Sigma Computing, or similar platforms.
• Exposure to MLOps frameworks, model monitoring, and responsible AI practices.