Senior 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 production systems.
• Build feature pipelines for model serving and ensure effective integration with front-end applications, databases, and back-end services.
• Mentor and guide machine learning engineers, fostering team growth through training and collaboration.
• Conduct code reviews to maintain quality and adhere to best practices.
• Manage end-to-end MLOps pipelines for data collection, model training, validation, and monitoring.
• 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 training and inference.
• Collaborate with applied scientists and analysts to convert model requirements into production-ready solutions.
• Establish monitoring and alerting systems for deployed models to ensure prompt issue resolution.
• 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.
• 6+ years of hands-on experience as a Machine Learning Engineer or Architect with a strong portfolio of deployed ML models for batch, streaming and realtime usecases
• Proficient in Python for model development and data manipulation, and experience with Java or 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

• Experience in large-scale production systems and distributed computing.
• Contributions to open-source projects or active participation in the ML community.
• Demonstrated leadership capabilities and experience in mentoring junior engineers.
• Innovative mindset with a track record of developing solutions leading to significant business improvements or patents.
• A collaborative approach to working across multiple products and application teams.
• A willingness to learn, share, and improve continuously.

Perks and Benefits

• Competitive compensation
• Generous stock options
• Medical Insurance coverage
• Work with some of the brightest minds from Silicon Valleys most dominant and successful companies