Senior Development Operations Engineer (Data Science)

Role Summary

We are seeking a Staff AI/ML Engineer & Data Scientist with deep expertise in traditional machine learning, Deep learning and strong MLOps experience to lead the design, deployment, and maintenance of production-grade ML systems. You will architect robust ML pipelines, apply advanced statistical techniques, and ensure models are accurate, explainable, and scalable. While the primary focus will be on traditional supervised, unsupervised, and time-series modeling, light experience with retrieval-augmented generation (RAG) is a plus.

MOST IMPORTANT SKILLS/RESPONSIBILITIES:

• 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 (anamoly prediction).
• End-to-End Model Development Lead the full lifecycle from data preprocessing and feature engineering to training, validation, deployment, and monitoring.
• Statistical Analysis Apply hypothesis testing, Bayesian methods, and model interpretability techniques to ensure reliable insights.
  
  - Masters degree or PHD is mandatory
  

Key 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.

Qualifications

Must Have:

• 8+ years of experience in applied ML or data science, including 3+ years in a senior or staff-level role.
• Expert proficiency in Python for ML development and Golang for backend integration.
• 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- MLFlow (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
  

Preferred:

• Exposure to retrieval-augmented generation (RAG) pipelines and vector databases.
• Time-series analysis and anomaly detection experience.
• Cloud deployment expertise (AWS, Azure, Google Cloud Platform).
• Familiarity with distributed computing frameworks (Spark, Ray).

Soft Skills

• Strategic problem-solver with the ability to align AI solutions to business goals.
• Excellent communicator across technical and non-technical stakeholders.