Machine Learning Engineer

Key Responsibilities

• Execute the R&D and product roadmap based on industry insights and business needs.• Collaborate with stakeholders to align ML solutions with business objectives.• Develop robust APIs and microservices for seamless ML model integration into productionsystems.• Build feature pipelines for model serving and ensure effective integration with front-endapplications, databases, and back-end services.• Mentor and guide machine learning engineers, fostering team growth through training andcollaboration.• Conduct code reviews to maintain quality and adhere to best practices.• Manage end-to-end MLOps pipelines for data collection, model training, validation, andmonitoring.• Ensure adherence to version control, testing, and model governance best practices.• Implement model compression, quantization, and distributed training techniques.• Track key metrics and optimize models post-deployment.• Work with cloud architects and DevOps to design scalable ML infrastructure.• Oversee deployment and management of compute and storage resources for model trainingand inference.• Collaborate with applied scientists and analysts to convert model requirements intoproduction-ready solutions.• Establish monitoring and alerting systems for deployed models to ensure prompt issueresolution.• Create and maintain documentation for ML architecture and best practices.• Stay current with ML technologies and contribute to ongoing enhancement efforts.

Required Qualifications

• Bachelors/ Masters / PhD in Computer Science or related field.
  • 5+ years of hands-on experience as a Machine Learning Engineer or Architect with a strongportfolio of deployed ML models for batch, streaming and realtime usecases• Proficient in Python for model development and data manipulation, and experience with Javaor Scala for building production systems.• Familiarity with messaging queues (e.g., Kafka, SQS) and MLOps tools (e.g., MLflow,Kubeflow, Airflow).• Experience with cloud platforms (AWS, Google Cloud, Azure) and containerization (Docker,Kubernetes).• Knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch) and data stores(e.g., Elasticsearch, MongoDB, PostgreSQL).• Knowledge of data processing and ETL tools (e.g., Apache Spark, Kafka).• Experience in monitoring tools Grafana and Prometheus• Strong problem-solving skills and analytical mindset.

Preferred Qualifications

• A willingness to learn, share, and improve continuously.

• IF INTERESTED FOR F2F INTERVIEW PLZ CALL ON 9113445563 TO DISCUSS AND SCHEDULE YOUR INTERVIEW.