<p><p><b>About the job :</b></p><p><br/></p><p>Location : Kochi / Bangalore (initially in Kochi)<br/><br/><b>Role Summary : </b></p><p><br/></p><p>We are looking for a hands-on AI/ML Engineer to design, build, and deploy production-grade machine learning systems.
The role requires strong experience in both model development and MLOps practices, ensuring that ML solutions are scalable, maintainable, and performance optimized.<br/><br/><b>Key Responsibilities :</b></p><p><br/></p><p>- Design and implement ML models for classification, regression, clustering, Recommendation System, Anomaly Detection, Neural Networks, NLP, Comp Vision Models<br/><br/>- Package models and deploy them into production environments using CI/CD workflows<br/><br/>- Develop and manage ML pipelines for data preprocessing, training, testing, and serving<br/><br/>- Monitor model performance in production (latency, drift, accuracy) and retrain when needed<br/><br/>- Collaborate with data engineers, MLOps engineers, and DevOps to ensure smooth deployments<br/><br/>- Support versioning of models, datasets, and configurations<br/><br/>- Participate in performance tuning, experiment tracking, and governance enforcement<br/><br/><b>Must-Have Skills :</b></p><p><br/></p><p>- Strong programming skills in Python, with experience in Scikit-learn, XGBoost, or TensorFlow/PyTorch<br/><br/>- Experience building and deploying models using Flask, FastAPI, or Streamlit<br/><br/>- Solid Grasp Of MLOps Concepts<br/><br/>- CI/CD for ML (GitHub Actions, Azure DevOps, etc.)<br/><br/>- Model versioning and registry (MLflow, SageMaker, Vertex AI, etc.)<br/><br/>- Data versioning (DVC, LakeFS)<br/><br/>- Monitoring and retraining workflows<br/><br/>- Comfortable working with containerization (Docker) and deployment on cloud platforms (AWS, Azure, or GCP)<br/><br/>- Good understanding of model explainability, fairness, and data privacy<br/><br/><b>Nice To Have :</b></p><p><br/></p><p>- Familiarity with Airflow, Kubeflow, or Prefect for orchestration<br/><br/>- Exposure to feature stores and drift detection tools<br/><br/>- Experience with NLP (LLMs, Transformers) or time series forecasting<br/><br/>- Understanding of model caching, load testing, and API security for ML services<br/><br/>- Domain Knowledge in Financial Services (especially Capital Markets)</p><br/></p> (ref:hirist.tech)