Job Summary
The AI/ML Engineer will design, develop, deploy, and manage machine learning solutions across the complete ML lifecycle. The role requires a strong Data Science background and hands-on experience with machine learning, statistical modeling, feature engineering, model development, deployment, monitoring, optimization, and MLOps practices. The engineer will operationalize ML models at scale through automated training and deployment pipelines, model versioning, experiment tracking, model monitoring, drift detection, and CI/CD, while collaborating with Data Scientists, Data Engineers, Cloud/DevOps teams, architects, and product stakeholders to deliver scalable and reliable AI/ML solutions. This is an onshore position requiring 5-10+ years of experience.
Key Responsibilities
• Design, develop, deploy, and manage machine learning solutions across the complete ML lifecycle.
• Develop and optimize machine learning models using supervised and unsupervised learning techniques.
• Perform feature engineering, model selection, hyperparameter tuning, validation, and performance evaluation.
• Manage ML solutions from experimentation through production deployment.
• Implement experiment tracking, model registry and versioning, dataset and model lineage, and reproducibility practices.
• Build and maintain model deployment and performance monitoring solutions.
• Implement data and model drift detection and automated retraining processes.
• Build automated ML pipelines and integrate them with CI/CD workflows.
• Apply MLOps principles and practices to operationalize machine learning models at scale.
• Collaborate with Data Scientists, Data Engineers, Cloud/DevOps teams, architects, and product stakeholders to deliver scalable AI/ML solutions.
• Support data engineering activities involving ETL/ELT pipelines, data quality, large datasets, and scalable data processing.
• Develop and maintain reliable AI/ML solutions across cloud environments.
Required Qualifications
• 5-10+ years of experience in AI/ML Engineering, Data Science, or related fields.
• Strong foundation in Data Science, Machine Learning, statistics, and predictive modeling.
• Hands-on experience with supervised and unsupervised machine learning 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.
• Experience with experiment tracking, model registry and versioning, dataset/model lineage, reproducibility, model deployment, performance monitoring, data/model drift detection, and automated retraining.
• Strong hands-on experience with MLOps principles, tools, and practices.
• Experience with one or more MLOps platforms or 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.
• Experience with at least one major cloud platform, including 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.
Preferred Qualifications
• 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.
