Job Description

About bluCognition bluCognition is an AI/ML start-up specializing in developing products and services to enhance data and analytical capabilities of our clients by leveraging the latest technology stack in AI and machine learning. Founded in 2017, by some very named senior professionals from the financial services industry, the company is headquartered in the US, with the delivery center based in Pune. Our clients are some of the biggest and the most progressive names in the financial services industry. Our client Zip.Co for whom we are hiring extensively, is a major global player in the consumer - buy now pay later segment and this role is to support their US business. The Machine Learning Engineering team is an emerging group that builds and manages infrastructure to support the development, optimization, experimentation, deployment, and maintenance of ML models. Our mission is to provide performant, reliable, scalable, and cost-efficient ML infrastructure that supports Zip\'s current and future business needs. As the Machine Learning Engineer in the MLE team, you will work closely with Sr ML Engineer, data scientists and other partner teams to deliver incredible experiences for Zip\'s customers by developing world-class AI/machine learning technology and MLOps capabilities. YOU\'LL ALSO: Develop, grow, and support the Machine Learning Engineering team Promote a high-performance culture Participate in roadmap and project planning Drive delivery and outcomes Delegate effectively Apply and promote best engineering practices on ML Ops Perform architectural design and help your team solve complex problems To help us level up, you\'ll bring: Technical excellence across design principles and code quality Focus on operational maturity and effective delivery Comfortable challenging directly whilst caring personally Flexibility, an open mind, curiosity, and passion for learning Our values in your DNA: Customer First, Own It, Stronger Together, and Change the Game Technical Skills: 2 to 5 years of experience building machine learning systems in applied settings; Understanding of common ML problems and solutions, such as classification, clustering, data processing, feature engineering, modeling, training, evaluation, etc. Experience in ML Ops, including the design, implementation, optimization, management, and maintenance of ML pipelines. Past experience in implementing ML systems is a plus. Experience in working with model monitoring systems. Familiarity with one or more areas: ML infrastructure, Ops, ML pipelines, algorithms, statistical methods, and analytics to solve real-world engineering problems Familiarity with industry standards and best practices in ML Ops, familiarity with (CI/CD) practices and tools is a plus but not necessary. Familiarity with building container-based systems; relational and dimensional data modeling techniques is a plus. Strong software engineering skills across multiple languages Python, R, C/C++, etc., and ML packages (e.g., scikit-learn, TensorFlow, Caffe, Theano, Torch) Experience in Azure infrastructure Experience in Agile Development, such as sprint planning, project management, etc. Excellent verbal and written communication skills Desirable: Experience with one or more Managed ML systems, such as Azure ML, ML flow Experience with one or more DBs: CosmosDB, MySQL etc. Bonus: Experience with feature stores such as Feast, Feathr, etc. Experience with programming languages: Python Experience with Apache Spark, Databricks, and Snowflake Data Cloud Plus: Experience with managing containerized services (Kubernetes, Docker, etc.) Experience with Microsoft Azure Cloud Services

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