Key Responsibilities:
• Lead the execution of the R&D and product roadmap, leveraging industry insights and business needs to drive ML initiatives while managing team priorities and timelines.
• Collaborate with cross-functional stakeholders to ensure alignment of ML solutions with overarching business objectives, effectively communicating technical concepts to non-technical audiences.
• Oversee the development of robust APIs and microservices, ensuring smooth integration of ML models into production environments, and guide the team in building feature pipelines for model serving.
• Mentor and develop machine learning engineers, fostering a positive and productive work environment through training, guidance, and constructive feedback.
• Conduct code reviews and establish best practices to maintain high quality and performance standards while promoting adherence to version control and model governance.
• Manage and optimize end-to-end MLOps pipelines for data collection, model training, validation, and monitoring, while ensuring team collaboration and effective resource allocation.
• Drive the implementation of model compression, quantization, and distributed training techniques to enhance performance, encouraging innovative solutions from team members.
• Track key metrics and optimize deployed models to ensure ongoing effectiveness, collaborating with team members to identify improvement opportunities.
• Collaborate with cloud architects and DevOps teams to design and maintain scalable ML infrastructure, ensuring effective resource management and deployment.
• Work closely with applied scientists and analysts to transform model requirements into production-ready solutions, facilitating teamwork across departments.
• Establish and maintain monitoring and alerting systems for deployed models, ensuring prompt issue resolution while guiding the team in best practices.
• Create and uphold documentation for ML architecture and best practices to ensure knowledge sharing within the team, promoting continuous improvement.
• Stay current with advancements in ML technologies and lead ongoing enhancement initiatives within the team, encouraging team participation in the ML community
Skills & Qualifications:
• Bachelors/ Masters / PhD in Computer Science or related field.
• 3-15 years of experience in machine learning, with a strong portfolio of deployed ML models for various use cases, including batch, streaming, and real-time.
• Proficient in Python for model development and data manipulation, with experience in 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 technologies (Docker, Kubernetes).
• Knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch) and database(e.g., Elasticsearch, MongoDB, PostgreSQL).
• Understanding of data processing and ETL tools (e.g., Apache Spark, Kafka).
• Experience with monitoring tools like Grafana and Prometheus.
• Strong problem-solving skills and an analytical mindset. experience with analytics, data science and big data experience in a business context.
• Experience managing large-scale production systems and distributed computing environments.
• An innovative mindset with a track record of developing solutions that yield significant business improvements or patents.
• A collaborative approach to working across multiple product and application teams, with excellent communication and conflict resolution skills.
• A commitment to continuous learning, sharing knowledge, and improving team practices.
Perks and Benefits:
• Competitive compensation
• Generous stock options
• Medical Insurance coverage
• Work with some of the brightest minds from Silicon Valley’s most dominant and successful
companies.
Bachelors
B.E
Cloud,Kafka,Machine learning,Machine learning and Artificial Intelligence,mongodb,Python,Machine learning Platforms,
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