Senior Machine Learning Engineer
3 weeks ago
We're MiQ, a global programmatic media partner for marketers and agencies. As a Lead - Machine Learning Ops in our data science department, you'll have the chance to:
Key Responsibilities- Lead a high performance MLOps team focusing on MLOps governance and making ML applications production ready
- Work with ML Engineers & data engineers to optimize & scale the AI/ML POCs developed by data scientists wrt latency, throughput, cloud cost, maintainability etc.
- Work with MLOps Engineers to design, deploy & monitor end-to-end ML applications
- Mentoring, coaching, providing feedback, building career plans and assessing performance for your direct reports enabling a high performance MLOps team
- Operationalize, grow & support MLOps practice at MiQ with assistance from data engineers, data scientists, devOps specialists.
- Lead automation initiatives across components of ML lifecycle following agile development practices - data quality, data processing, model development, testing, model monitoring, ML platform adoption tracking & business value measurement
- 5+ years of experience with designing and building robust, highly scalable and highly available data driven pipelines and applications
- 3+ years of experience with ML lifecycle management including - model versioning, model and data lineage, model monitoring, model hosting and deployment, scalability, orchestration, continuous training & deployment, and automated pipelines
- 3+ years of hands-on experience in building scalable distributed systems for model training, inferencing (batch & real time) & evaluation of machine learning models
- Strong ability to design end to end ML systems and lead a technical roadmap, work with cross functional teams, with proven capacity to influence and build alignment
- Deep understanding of machine learning best practices
- Experience with machine learning models integration with web applications
- Strong DevOps mentality: Knowledge of making a complicated pipeline simple and easy to maintain, with automated governance
- Software engineering fundamentals: version control systems, web services & APIs
- Proficiency in Linux, Docker, Kubernetes, Jenkins, Version control systems
- Good knowledge of machine learning & deep learning architectures, modelling frameworks & libraries
- Experience with hardware resource management for ML training and/or deployment
- Experience in using MLOps frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc. and building feature store
- 5+ years of experience programming with Python or Spark or Java with strong understanding of data structures, algorithms & performance complexity
- 5+ experience working with SQL databases, data warehouses & data lakes
- 3+ years experience working with cloud infrastructure
- Experience with CI/CD systems, agile development processes, test driven development
- Excellent data analytical & debugging skills; problem solving, critical thinking & communication skills with strong attention to detail
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