Staff AI/ML Engineer & Data Scientist (Remote)

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. The individual needs to have devops experience for setting up databases, CI/CD (Databricks end‑to‑end experience is plus).

MOST IMPORTANT SKILLS/RESPONSIBILITIES

Strong databricks MLOPS, databricks AI/ML, and aws MLOPS and software experience

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 (anomaly prediction). The vast majority of data generated today is unlabeled

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.

Devops Experience

– Experience with database setup, Databricks, AWS, CI/CD, DevOps/MLOps, VectorDBs, GraphDB

Masters degree or PhD is mandatory

This role requires analysis of manufacturing, sensors, PLC data; prior experience would be a plus

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.

Devops Experience

– Experience with database setup, Databricks, AWS, CI/CD, DevOps/MLOps, VectorDBs, GraphDB

Qualifications

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 (Good to have: 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 (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, 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

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