Machine learning Engineer at Weekday AI

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

11 Jan, 26

Salary

0.0

Posted On

13 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Data Science, Python, TensorFlow, PyTorch, Scikit-learn, XGBoost, Data Manipulation, SQL, Cloud Platforms, AWS, GCP, Azure, Docker, Kubernetes, MLOps

Industry

technology;Information and Internet

Description

This role is for one of Weekday’s clients Min Experience: 4 years Location: Bangalore JobType: full-time We are seeking an experienced and innovative Machine Learning Engineer to join our growing data science and AI team. In this role, you will design, build, and deploy scalable ML models and pipelines that power intelligent decision-making across our products and platforms. You’ll work closely with data scientists, software engineers, and product teams to transform complex data into actionable insights and automated solutions. As a Machine Learning Engineer, you’ll play a key role in operationalizing models, optimizing performance, and ensuring reliability in production environments. You’ll also explore new algorithms, experiment with modern ML techniques, and help shape the strategic direction of our AI initiatives. Key Responsibilities Design, develop, and deploy machine learning models for predictive analytics, classification, recommendation, and anomaly detection. Build scalable data pipelines for feature extraction, model training, and evaluation using modern ML frameworks and tools. Collaborate with data scientists to convert research prototypes into production-ready models. Implement end-to-end ML systems, from data ingestion and preprocessing to model deployment and monitoring. Continuously evaluate and improve model performance through tuning, retraining, and data-driven optimization. Work with software and platform teams to integrate ML outputs into core applications and APIs. Develop automated CI/CD workflows for ML experiments and production deployments. Ensure data and model governance, including version control, reproducibility, and explainability. Stay up to date with emerging trends in machine learning, deep learning, and MLOps, bringing innovative ideas to the team. Partner with stakeholders to identify business challenges that can be solved with ML-driven approaches. Key Skills and Qualifications Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or related field. 4+ years of hands-on experience in building and deploying machine learning models in real-world applications. Strong knowledge of machine learning algorithms, statistical modeling, and data preprocessing techniques. Proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or XGBoost. Expertise in data manipulation and analysis using Pandas, NumPy, and SQL. Experience with end-to-end ML pipelines, including data engineering, model training, testing, and deployment. Working knowledge of cloud platforms such as AWS, GCP, or Azure for ML workloads. Familiarity with containerization and orchestration tools like Docker and Kubernetes is a plus. Strong analytical and problem-solving skills with attention to scalability and performance. Excellent communication skills and ability to collaborate across technical and business teams. Preferred Qualifications Experience in MLOps tools like MLflow, Kubeflow, Airflow, or SageMaker. Understanding of deep learning architectures for NLP or computer vision. Exposure to feature store management, model versioning, and monitoring frameworks. Passion for continuous learning and applying ML to real-world business problems.

Responsibilities

Design, develop, and deploy machine learning models for various applications. Collaborate with teams to operationalize models and ensure their performance in production environments.