Senior Machine Learning Engineer

• Collaborate with key stakeholders to identify business challenges, translating ambiguous problems into structured analyses using statistical modelling and machine learning algorithms.
• Lead the selection, validation, and optimization of models to discover meaningful patterns and insights, ensuring models remain relevant, reliable, and scalable.
• Drive continuous integration and deployment of data science solutions, optimizing performance through advanced machine learning techniques, code reviews, and best practices.
• 'Develop and deliver sophisticated visualizations, dashboards, and reports translate complex data into clear, actionable insights for business stakeholders.
• Present technical solutions to business stakeholders, using creative methods to explain complex concepts, increase understanding, and encourage solution adoption.
• Mentor and develop junior data scientists, fostering a culture of continuous learning, knowledge sharing, and skills development within the organization.
• Write clean, high-quality code, ensuring all outputs pass quality assurance checks, and contribute to the development of novel solutions to solve complex business problems.
• Stay informed on industry trends, emerging tools, and techniques, applying them to improve data science practices and encourage innovation within the team.
• Lead strategy development for one or more data products, managing roadmaps, identifying requirements, and collaborating with business stakeholders to ensure alignment with business goals.

• Machine Learning models for Advanced D&A Americas.

• Data products initiatives for Advanced D&A Americas.

• GenAI initiatives for Advanced D&A Americas.

• Build, maintain, and optimize end to end ML pipelines covering data ingestion, feature engineering, training, evaluation, deployment, inference and monitoring using Databricks and related tooling.

• Collaborate closely with Data Scientists to translate experimental and research grade models into reliable, scalable, and secure production services that meet business and technical requirements.

• Apply MLOps best practices including model versioning, experiment tracking, monitoring, and automated deployments.

• Develop and deploy traditional ML models (e.g., regression, classification, forecasting, NLP) to solve business problems.

• Implement runtime monitoring dashboards and alerting mechanisms to detect performance degradation, data anomalies, and system failures in near real time.

• Support AI / GenAI initiatives, including LLM based prototypes and production workflows where applicable.

• Collaborate with product owners, data scientists, engineers, and business stakeholders to define model requirements, SLAs, success metrics, and deployment constraints.

• Integrate ML solutions into downstream systems via APIs, batch pipelines, or event driven processes.

• Write high quality, maintainable code following engineering best practices, with version control and CI/CD in Bitbucket.

• Troubleshoot and optimize model performance, scalability, latency, and cost in production environments.

• Provide guidance and best practices to data scientists and engineers on production ready ML development and MLOps workflows.

• Evaluate emerging tools, frameworks, and practices to enhance the organization’s ML and GenAI operational maturity.

• ML models are reliable, scalable, and observable in production environments

• Reduced time and friction moving from experimentation to production ML systems

• High availability and reliability of ML pipelines and inference services

• Strong collaboration with Data and cross functional teams resulting in business impacting ML solutions

• Clear observability into model performance, data quality, and system health

• Adoption of standardized patterns for ML development and deployment across the team

• Bachelor’s or master’s degree in computer science, Engineering, Data Science, Mathematics, or a related field, or 3+ years of equivalent professional experience in a related role

• Strong foundation in machine learning algorithms and applied modeling techniques

• Demonstrated ability to build and operate production grade software systems is a plus

• Proven ability to work in ambiguous problem spaces and evolving AI landscapes

• 3+ years of experience in Machine Learning Engineering, Applied Machine Learning, or a closely related role

• Hands on experience deploying and supporting ML models in production

• Proven experience using ML lifecycle management tools such as MLflow (preferred) or similar platforms

• Experience using Databricks or similar platforms for data processing and ML workloads

• Proven collaboration with Data Scientists and Engineers in cross functional teams

• Experience supporting both early stage experimentation and production systems

• Strong understanding of supervised and unsupervised learning techniques

• Feature engineering, model evaluation, and performance optimization

• Experience operationalizing models beyond notebooks

• Building and maintaining ML pipelines (training, inference, retraining)

• Model versioning, experiment tracking, and reproducibility

• Monitoring for model performance, data drift, and pipeline failures

• CI/CD practices for ML workflows

• Strong proficiency in Python

• Writing testable, maintainable, production quality code

• Git based version control workflows

• Experience integrating ML into applications or services

• Exposure to LLMs, embeddings, prompt engineering, or retrieval augmented generation (RAG)

• Experience moving GenAI use cases from prototype to production

• Familiarity with evaluating GenAI outputs and monitoring cost, latency, and quality

• Experience building or consuming REST APIs for model inference

• Understanding of distributed systems and data pipelines

CHEP