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, Tensor Flow, XGBoost).
- Robust 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, Tensor Flow, 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 (Lang Chain, Lang Graph).
- Familiarity with causal ML and graph-based reasoning for root-cause analysis.
- Experience with feature stores, streaming inference, and event-driven ML.
- Knowledge of Open Telemetry, 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, Tensor Flow, XGBoost, Pandas, Num Py, Sci Py, 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

📌 Senior Engineer ML (Bengaluru)
🏢 Albertsons Companies India
📍 Bengaluru