Senior Machine Learning Engineer ; Hybrid

Position Purpose

We are seeking a Senior Machine Learning Engineer to design, build, deploy, and operate scalable machine learning and AI solutions in production. This role sits at the intersection of MLOps, traditional data science modeling, and software engineering, with opportunities to work on AI/GenAI engineering use cases.

• 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.

Internal:
Data & Analytics Americas, Processes Digitalization, Supply Chain, Commercial, Serialization+, Finance, Digital

• Bachelor’s or master’s degree in computer science, engineering, data science, mathematics, or a related field, or 7+ 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.

• 5+ 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.

Salary range: $103,000 to $140,000 per year. Other forms of compensation may be part of a total offering beyond medical & retirement benefits.