Excellent Opportunity For AI Developer

ML Engineer - Responsibilities (Senior-Level, 6-8 YOE)

• This professional is a specialized, infrastructure-focused expert responsible for the end-to-end lifecycle of machine learning models. Their mandate is to ensure that predictive models are not just developed as proofs-of-concept, but are deployed, monitored, scaled, and maintained in a robust, automated, and reliable production environment.5 Their primary deliverable is a stable, scalable Machine Learning Operations (MLOps) platform and the automated pipelines that enable the entire data science organization.

ML Engineer Skill Matrix (Senior-Level, 6-8 YOE)

Skill Area

Core Competency

Key Tools & Platforms

Strategic Responsibility

MLOps & Automation

CI/CD/CT Pipeline Architecture, Infrastructure as Code (IaC), Workflow Orchestration

Jenkins, GitLab CI, GitHub Actions, Terraform, Ansible, Airflow, Kubeflow Pipelines, Prefect

Architecting the end-to-end automated ML lifecycle. Defining the standards for how models move from research to production. Mentoring teams on DevOps best practices for ML.

Model Lifecycle Management

Model & Data Versioning Strategy, Experiment Tracking & Management, Model Registry Governance

MLflow, DVC, Pachyderm, Weights & Biases, Comet ML, Vertex AI Model Registry

Establishing and enforcing organization-wide governance for model and data assets to ensure reproducibility, auditability, and compliance. Designing the central model registry.

Production Monitoring & Explainability

Data & Concept Drift Detection, Statistical Performance Monitoring, Anomaly Detection, Model Explainability (SHAP, LIME)

Evidently AI, Fiddler, Arize, Prometheus, Grafana, Custom monitoring scripts

Designing a proactive, comprehensive monitoring strategy that goes beyond simple accuracy metrics to ensure model reliability, fairness, and business alignment. Defining alerting policies and automated response actions.

Scalable Computing & Data Processing

Distributed Data Processing, Distributed Model Training, Container Orchestration, Cloud ML Platforms

Apache Spark, Ray, Horovod, Kubernetes, Docker, AWS SageMaker, Google AI Platform, Azure Machine Learning

Designing cost-effective, scalable, and resilient infrastructure for both training and serving large-scale models. Making build-vs-buy decisions on ML platform components.

Core ML & Statistical Rigor

Advanced ML Algorithms, Statistical Hypothesis Testing, A/B Testing for Model Comparison, Causal Inference Concepts

Scikit-learn, Statsmodels, XGBoost, Python (NumPy, SciPy)

Guiding the model validation strategy for the entire team. Designing and analyzing rigorous online experiments (A/B tests) to scientifically prove the business impact of new models before full rollout.

Leadership & Mentorship

Technical Leadership, Project Management, Cross-functional Partnership, Mentoring Junior Engineers

N/A

Setting technical direction for a team, driving consensus on architectural decisions, and successfully partnering with data science, product, and business teams. Growing the skills of the ML engineering team.