ML Engineer
Indexed description
- Develop, train, evaluate, and deploy Machine Learning models for enterprise and product use cases.
- Design and implement solutions for regression, classification, clustering, anomaly detection, and predictive analytics.
- Build reusable feature engineering, data preprocessing, and model training pipelines.
- Work with large-scale structured and unstructured datasets using Python and SQL.
- Collaborate with Data Engineers and AI Engineers to productionize Machine Learning models.
- Build end-to-end ML pipelines covering data ingestion, training, validation, deployment, inference, monitoring, and automated retraining.
- Deploy Machine Learning models as REST APIs using FastAPI, Flask, or similar frameworks.
- Optimize models for accuracy, scalability, reliability, and production performance.
- Monitor deployed models for prediction quality, model drift, and retraining requirements.
- Containerize ML applications using Docker and support automated deployments through CI/CD pipelines.
- Participate in architecture discussions, code reviews, technical documentation, and continuous improvement initiatives.
- Collaborate with Product Managers, Software Engineers, Data Engineers, and business stakeholders to deliver AI-driven solutions.
- 3+ years of experience in Machine Learning Engineering, Applied AI, or Data Science.
- Strong proficiency in Python, SQL, Scikit-learn, TensorFlow or PyTorch, NumPy, and Pandas.
- Strong understanding of supervised and unsupervised learning, regression, classification, clustering, anomaly detection, feature engineering, model evaluation, and hyperparameter tuning.
- Hands-on experience building and deploying Machine Learning models using FastAPI or Flask REST APIs.
- Experience working with Docker, CI/CD pipelines, Git, and cloud platforms such as Azure, AWS, or GCP.
- Good understanding of scalable ML systems, model optimization, monitoring, model drift detection, and retraining strategies.
- Strong analytical, debugging, and problem-solving skills.
- Excellent communication and stakeholder collaboration abilities.
- Ability to work effectively in agile, cross-functional engineering teams.
- Experience with MLflow, Kubeflow, Apache Airflow, Azure Machine Learning, or AWS SageMaker.
- Exposure to NLP, Computer Vision, or Recommendation Systems.
- Familiarity with Apache Spark or Hadoop.
- Knowledge of Responsible AI, Model Explainability, AI Fairness, Bias Detection, and AI Governance.
- Experience with Kubernetes or container orchestration platforms.
- Domain experience in Manufacturing, Automotive, Supply Chain, Financial Services, Healthcare, or Enterprise Analytics is an added advantage.
Perks Of Working With Us
- Clear objectives to ensure alignment with our mission.
- Opportunities to collaborate closely with customers, product managers, and leadership.
- Continuous learning through Nexversity and structured mentorship.
- Hybrid work model promoting flexibility and work-life balance.
- Comprehensive family health insurance coverage.
- Accelerated career growth and opportunities to work with emerging AI technologies.
Join our passionate team and tailor your growth with us!
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