Senior Engineer ML

Senior Engineer Machine Learning

KEY RESPONSIBILITIES:

• Design, train, evaluate, and deploy ML models for anomaly detection, incident prediction, alert classification, and signal correlation.
• Conduct exploratory data analysis, statistical modeling, and data visualization to uncover patterns and inform model development.
• Build robust feature engineering, validation, and inference pipelines that operate at scale.
• Architect and maintain model-serving infrastructure, online scoring services, and batch prediction workflows.
• Design, execute, and analyze A/B tests and controlled experiments to validate model impact and guide product decisions.
• Monitor model drift, latency, throughput, and operational health in production; define SLOs and runbooks.
• Own model performance and reliability; lead root cause analysis for model failures and data pipeline incidents.
• Build and optimize microservices and APIs for model inference, agent orchestration, and event processing.
• Partner with SRE, platform, and data teams to ensure seamless integration, scalability, and cost efficiency.
• Drive MLOps best practices: versioning, experiment tracking, automated retraining, and CI/CD for ML.
• Apply rigorous experimental design and statistical methods to validate hypotheses and ensure reproducibility.
• Mentor junior and mid-level engineers on production ML engineering, system design, data science best practices, and debugging.
• Contribute to technical documentation, architectural decision records, and operational playbooks.

REQUIRED QUALIFICATIONS:

• Bachelor's degree in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field.
• 8+ years of experience in machine learning engineering, software engineering, data science, or applied research.
• 4+ years of hands-on experience building, deploying, and operating ML systems in production.
• Expert-level Python and deep familiarity with ML frameworks (PyTorch, TensorFlow, XGBoost).
• Strong foundation in statistical analysis, experimental design, and exploratory data analysis.
• Strong understanding of MLOps, model monitoring, and distributed systems fundamentals.
• Demonstrated ability to derive insights from large-scale datasets and deliver high-impact ML capabilities in cross-functional enterprise teams.

MANDATORY SKILLS:

• Advanced Python and production ML frameworks (PyTorch, TensorFlow, XGBoost)
• End-to-end ML pipeline design: feature engineering, training, validation, inference, and monitoring
• Statistical analysis, hypothesis testing, and experimental design
• Data exploration, visualization, and communication of insights to diverse stakeholders
• A/B testing and causal inference for model and product evaluation
• MLOps practices: model versioning, experiment tracking, CI/CD for ML, and automated deployment
• Model monitoring, drift detection, latency optimization, and production debugging
• Docker, Kubernetes, and cloud-native microservices architecture
• REST API design and scalable backend services for real-time and batch inference
• Anomaly detection, time-series modeling, and signal classification for observability
• Observability platform integration and production incident management for ML systems
• System design for high-availability, low-latency, and cost-efficient ML services
• SQL and large-scale data manipulation

PREFERRED QUALIFICATIONS:

• Master's in Computer Science, Statistics, Mathematics, or a related quantitative field.
• Experience with LLM-powered applications and agentic frameworks (LangChain, LangGraph).
• Familiarity with causal ML and graph-based reasoning for root-cause analysis.
• Experience with feature stores, streaming inference, and event-driven ML.
• Knowledge of OpenTelemetry, Grafana, Prometheus, and SRE operating models.
• Exposure to multi-cloud environments (Azure, AWS, GCP) and hybrid deployments.
• Experience with data visualization tools and frameworks.
• Published research, patents, or open-source contributions in ML, data science, or systems engineering.

KEY SKILLS:

• Python, PyTorch, TensorFlow, XGBoost, Pandas, NumPy, SciPy, Scikit-learn
• SQL, large-scale data processing, exploratory data analysis
• Statistical modeling, hypothesis testing, experimental design, A/B testing
• Data visualization (Matplotlib, Seaborn, Plotly) and storytelling with data
• MLOps, Docker, Kubernetes, microservices, CI/CD
• Model monitoring, drift detection, performance tuning
• Observability, SRE collaboration, production ownership