Machine Learning Engineer

Job Description Job Title: Staff AI/ML Engineer & Data Scientist Schedule: 9-6 Central Time (1 hour non-billable lunch) M-F Masters degree or PHD is mandatory 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. 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). Preferred: • 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). Soft Skills: • Strategic problem-solver with the ability to align AI solutions to business goals. • Excellent communicator across technical and non-technical stakeholders.