AI/ML Engineer & Data Scientist

Job timing - 9-6 Central Time | 7:30pm to 4:30am IST

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 MLOPS in Databricks
Databricks AI/ML
AWS MLOPS
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/ML Ops, VectorDBs, GraphDB
At least 5 years of experience in software development is mandatory.
Bachelors/master's in computer science is mandatory
2-3 years of data architect experience in Databricks is mandatory
This role required analysis of manufacturing, sensors, PLC data, any prior would be a plus
This role will work on project like predictive Maintenance

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.
Dev ops Experience – Experience with database setup, Databricks, AWS, CI/CD, DevOps/MLOps, vectorDBs, GraphDB

Qualifications

Must Have:

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).
Master's 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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