Lead - Machine Learning Engineer - Python, ML Frameworks, MLOps

Position - Lead - Machine Learning Engineer - Python, ML Frameworks, MLOps, Containerization, Terraform, GCP, Vertex AI, IBM Watsonx

About this Role:

We are seeking a visionary Lead Machine Learning Engineerto architect, guide, and deliver enterprise-grade ML solutions that drive strategic business outcomes. You will lead cross-functional teams, define technical direction, and ensure the robustness, scalability, and reliability of ML systems across the full lifecycle.

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. Youll 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 UPSs 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:

EducationBachelors or Masters degree in Computer Science, Engineering, Mathematics, or related field (PhD preferred).

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).
• Expertise in data/model governance, lineage tracking, and compliance frameworks.
• Contributions to open-source ML/MLOps libraries or active participation in ML communities.
• Domain experience in logistics, supply chain, or large-scale consumer platforms.
• Experience presenting technical solutions to executive stakeholders.