Senior AI/ML MLOps Engineer
About The Position
Our client is looking for an experienced AI/ML Engineer with a strong Data Science background to design, develop, deploy, and manage machine learning solutions across the complete ML lifecycle. The candidate should have hands-on experience in machine learning, statistical modeling, feature engineering, model development, deployment, monitoring, and optimization. The role requires strong experience with MLOps practices to operationalize ML models at scale, including automated training and deployment pipelines, model versioning, experiment tracking, model monitoring, drift detection, and CI/CD. The candidate will work closely with Data Scientists, Data Engineers, Cloud/DevOps teams, architects, and product stakeholders to deliver scalable and reliable AI/ML solutions.
Requirements
- Strong foundation in Data Science, Machine Learning, statistics, and predictive modeling.
- Hands-on experience with supervised and unsupervised ML techniques.
- Strong understanding of feature engineering, model selection, hyperparameter tuning, validation, and performance evaluation.
- Experience with ML frameworks such as Scikit-learn, TensorFlow, PyTorch, and XGBoost.
- Strong proficiency in Python and familiarity with SQL.
- Demonstrated experience managing ML solutions from experimentation through production, including: Experiment tracking, Model registry and versioning, Dataset/model lineage, Reproducibility, Model deployment, Model performance monitoring, Data/model drift detection, Automated retraining.
- Strong hands-on experience with MLOps principles, tools, and practices.
- Experience with one or more platforms/tools such as MLflow, Kubeflow, Azure Machine Learning, AWS SageMaker, Google Vertex AI, or Databricks.
- Experience building automated ML pipelines and integrating them with CI/CD workflows.
- Experience with Docker, Kubernetes, Git, and CI/CD platforms such as Azure DevOps, GitHub Actions, Jenkins, or similar tools.
- Hands-on experience with end-to-end lifecycle automation using Kubeflow and Vertex AI, MLflow-based experiment/model tracking and registration, and Evidently-based model/data drift detection.
- Experience with at least one major cloud platform: Azure, AWS, or GCP.
- Understanding of data engineering concepts, ETL/ELT pipelines, and data quality.
- Experience working with SQL/NoSQL databases and large datasets.
- Familiarity with scalable data processing and cloud storage technologies.
Nice To Haves
- Experience with Generative AI, LLMs, RAG, NLP, or conversational AI.
- Exposure to LLMOps and GenAI lifecycle management.
- Experience with Databricks and/or Spark.
- Knowledge of feature stores and data/model versioning tools.
- Familiarity with model governance, explainability, responsible AI, and AI security.
- Experience developing AI/ML applications in the healthcare domain.
- Familiarity with Agile/Scrum delivery models.
- Experience covering model development, fine-tuning, evaluation, data pipelines, feature engineering, and end-to-end AI solution development.
Responsibilities
- Design, develop, deploy, and manage machine learning solutions across the complete ML lifecycle.
- Operationalize ML models at scale using MLOps practices.
- Implement automated training and deployment pipelines.
- Manage model versioning, experiment tracking, model monitoring, and drift detection.
- Integrate ML solutions with CI/CD workflows.
- Collaborate with Data Scientists, Data Engineers, Cloud/DevOps teams, architects, and product stakeholders.
- Deliver scalable and reliable AI/ML solutions.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
