Engineer ML

Engineer Machine Learning

ROLE SUMMARY:

Individual contributor responsible for developing, testing, and supporting machine learning components of the AIOps platform. Works under the guidance of senior engineers to build models, prepare data, and integrate ML services into observability workflows. This is a junior-to-mid-level role requiring 13 years of hands-on experience.

KEY RESPONSIBILITIES:

• Assist in designing and developing ML models for anomaly detection, alert classification, and forecasting.
• Build and maintain data preprocessing and feature engineering pipelines.
• Support model training, validation, and performance evaluation against defined metrics.
• Integrate ML microservices with observability platforms, APIs, and event systems.
• Monitor model behavior in staging and production environments and report deviations.
• Document model logic, experiments, training parameters, and deployment procedures.
• Collaborate with data, platform, and SRE teams to resolve data quality and inference issues.
• Adopt MLOps best practices for versioning, reproducibility, and code quality.

REQUIRED QUALIFICATIONS:

• Bachelors degree in Computer Science, Engineering, Data Science, or a related field.
• 13 years of experience in software engineering, data science, or machine learning.
• Proficiency in Python and common ML libraries (scikit-learn, Pandas, NumPy).
• Basic understanding of the ML lifecycle: training, validation, inference, and evaluation.
• Familiarity with SQL and relational data manipulation.
• Exposure to REST APIs, microservices concepts, and Git version control.

MANDATORY SKILLS:

• Python programming for data science and machine learning
• scikit-learn, Pandas, NumPy, and foundational feature engineering
• SQL and relational data querying for analysis and feature extraction
• Data preprocessing, cleaning, and exploratory data analysis
• Basic model training, validation, and evaluation metrics (precision, recall, RMSE, etc.)
• REST API integration and Git-based collaborative development
• Docker fundamentals and containerized local development
• Understanding of observability data types: logs, metrics, traces, and alerts

PREFERRED QUALIFICATIONS:

• Exposure to deep learning frameworks (PyTorch or TensorFlow).
• Experience with time-series analysis or statistical anomaly detection.
• Familiarity with MLOps tools (MLflow, Kubeflow) and CI/CD pipelines.
• Knowledge of OpenTelemetry and monitoring platforms such as Grafana or Prometheus.
• Experience with NoSQL databases or vector stores.
• Coursework or certifications in machine learning, statistics, or cloud technologies.

KEY SKILLS:

• Python, scikit-learn, Pandas, SQL
• Data preprocessing, feature engineering, model evaluation
• REST APIs, Docker, Git, observability fundamentals