Senior/Lead MLOps

Job Title:

SR/Lead Machine Learning Engineer – Python, ML Frameworks, MLOps, Containerization, Terraform, GCP, Vertex AI, IBM Watsonx

About this Role:

Sr/Lead Machine Learning Engineer

As a Lead MLE, you will play a pivotal role in shaping our ML platform strategy, mentoring senior engineers, and driving adoption of best practices in MLOps, model governance, and responsible AI. You’ll collaborate with stakeholders across data science, engineering, and product to translate complex business challenges into intelligent systems.

Key Responsibilities:

• Lead the design

  , development, and deployment of scalable ML models and pipelines for high-impact business applications.
• Architect ML systems using Vertex AI Pipelines, Kubeflow, Airflow, and manage infrastructure-as-code with Terraform/Helm.
• Define and implement strategies for automated retraining, drift detection, and model lifecycle management.
• Oversee CI/CD workflows for ML, ensuring reliability, reproducibility, and compliance.
• Establish standards for model monitoring, observability, and alerting across accuracy, latency, and cost.
• Drive integration of feature stores, vector databases, and knowledge graphs for advanced ML/RAG use cases.
• Ensure security, compliance, and cost-efficiency across ML pipelines and infrastructure.

• Champion MLOps best practices

  and lead initiatives for reproducibility, versioning, lineage tracking, and governance.

• Mentor and coach

  senior/junior engineers, fostering a culture of technical excellence and innovation.
• Stay ahead of

  emerging ML technologies

  and evaluate their applicability to UPS’s ecosystem.
• Collaborate with leadership, product managers, and domain experts to align ML initiatives with strategic goals.
• Contribute to long-term ML platform architecture and roadmap planning.

Required Qualifications:

Education

Experience

• 8+ years of experience in machine learning engineering, MLOps, or large-scale AI/DS systems.
• Proven track record of leading ML projects from conception to production.

• Deep expertise in Python

  (scikit-learn, PyTorch, TensorFlow, XGBoost) and SQL.
• Experience

  architecting ML systems

  in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).
• Strong background in

  containerization

  (Docker, Kubernetes),

  orchestration

  (Airflow, TFX, Kubeflow), and

  infra-as-code

  (Terraform/Helm).
• Experience in

  big data and streaming technologies

  (Spark, Flink, Kafka, Hive, Hadoop).
• Hands-on experience with model observability tools (Prometheus, Grafana, EvidentlyAI) and Governance platforms (WatsonX).
• Strong understanding of ML algorithms, deep learning architectures, and statistical methods.
• Demonstrated leadership in mentoring teams and influencing technical direction.

Preferred Qualifications:

• Experience with real-time inference systems or low-latency streaming platforms.
• Hands-on with enterprise ML platforms

  (IBM WatsonX, GCP Vertex AI)

  and feature stores.
• Knowledge of model interpretability and fairness frameworks (SHAP, LIME, Fairlearn).