Senior Development Operations Engineer (Data Science)

The hiring company is confidential. The Staff AI/ML Engineer & Data Scientist will lead the design, deployment, and maintenance of production-grade machine learning systems, including model development, MLOps pipelines, and statistical analysis.

Responsibilities:

• ML Technical Leadership – Define ML architecture, best practices, and performance standards for enterprise-scale solutions
• End-to-End Model Development – Lead the full lifecycle from data preprocessing and feature engineering to training, validation, deployment, and monitoring
• Traditional ML Expertise – Apply algorithms such as regression, tree-based models, SVMs, clustering, and forecasting to solve high-impact problems ,feature engineering and hyper parameter tuning
• Programming & Integration – Build scalable ML pipelines and APIs in Python (primary) and Golang (for backend services)
• MLOps Implementation – Design and manage CI/CD pipelines for ML, including automated retraining, model versioning, monitoring, and rollback strategies
• Statistical Analysis – Apply hypothesis testing, Bayesian methods, and model interpretability techniques to ensure reliable insights
• Cross-Functional Collaboration – Partner with engineering, analytics, and product teams to align technical solutions with business objectives
• Devops Experience – Experience with database setup, databricks, aws, CI/CD, DevOps/MLOps, vectorDBs, GraphDB

Requirements:

• Atleast 5 years of experience in software development is mandatory
• Bachelors/Masters in computer Science is mandatory
• 2-3 years of data architect experience in Databricks is manadatory
• This role required analysis of manufacturing, sensors, PLC data
• 8+ years of experience in applied ML or data science, including 3+ years in a senior or staff-level role and devops experience
• Expert proficiency in Python for ML development
• Proven experience deploying traditional ML models to production with measurable business impact
• Strong knowledge of ML frameworks (Scikit-learn, XGBoost, LightGBM) and data libraries (Pandas, NumPy, Statsmodels)
• Hands-on MLOps experience with tools like MLflow (preferred), Databricks(preferred), Kubeflow, Vertex AI Pipelines, or AWS SageMaker Pipelines
• Experience with model monitoring, drift detection, and automated retraining strategies
• Strong database skills (SQL and NoSQL)
• Masters degree or PHD is mandatory
• Strategic problem-solver with the ability to align AI solutions to business goals
• Excellent communicator across technical and non-technical stakeholders
• light experience with retrieval-augmented generation (RAG) is a plus
• Databricks end-to-end experience is plus
• This role required analysis of manufacturing, sensors, PLC data, any prior would be a plus
• Golang for backend integration
• Exposure to retrieval-augmented generation (RAG) pipelines and vector databases
• Time-series analysis and anomaly detection experience
• Cloud deployment expertise (AWS, Azure, GCP)
• Familiarity with distributed computing frameworks (Spark, Ray)