Senior Engineer Machine Learning

nKEY RESPONSIBILITIES:

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

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

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- Advanced Python and production ML frameworks (PyTorch, TensorFlow, XGBoost)
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- End-to-end ML pipeline design: feature engineering, training, validation, inference, and monitoring
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- Statistical analysis, hypothesis testing, and experimental design
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- Data exploration, visualization, and communication of insights to diverse stakeholders
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- A/B testing and causal inference for model and product evaluation
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- MLOps practices: model versioning, experiment tracking, CI/CD for ML, and automated deployment
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- Model monitoring, drift detection, latency optimization, and production debugging
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- Docker, Kubernetes,



and cloud-native microservices architecture
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- REST API design and scalable backend services for real-time and batch inference
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- Anomaly detection, time-series modeling, and signal classification for observability
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- Observability platform integration and production incident management for ML systems
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- System design for high-availability, low-latency, and cost-effective ML services
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- SQL and large-scale data manipulation
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nPREFERRED QUALIFICATIONS:

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

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