ML Ops Engineer (HealthCare project, Sign-on bonus 1k) Offline

Requirements:

- At least 4 years’ professional experience in the data and analytics domain working in any capacity from data engineering and business intelligence to data science and machine learning;

- Upper-Intermediate written and spoken English.

Data Science:

- At least 2 years’ experience deploying advanced analytics models in a serverless/containerized environment;

- At least 2 years’ experience with data visualization tools like Cognos, Arcadia, Tableau;

- At least 2 years’ experience with classic ML approaches, NLP, & Deep Learning proficiency;

- At least 2 years’ experience developing enterprise solutions inclusive of Application Monitoring, Build Tools, Version Control, Unit Test, TDD, Change Management to support DevOps.

Data Engineering:

- At least 2 years’ experience with Spark programming (PySpark or Scala);

- At least 2 years’ experience with designing and developing Data Pipelines for Data Ingestion or Transformation using Java or Scala or Python;

- At least 2 years’ experience with SQL and Shell Scripting experience;

- At least 2 years’ experience with Streaming using Spark or Flink or Kafka

Preferred;

Data Engineering/ Data Science:

- Familiarity with operationalizing ML algorithms into production business processes: e.g., experience with containerization (such as Docker, Kubernetes, or Open Shift), competency with MLOps tools (such as Kubeflow, MLflow, Seldon, or Paperspace/Gradient);

- Familiarity with designing, building, and deploying production-level data pipelines using tools from Hadoop stack. Comfortable developing applications that make use of Hive/Impala, HBase, Oozie, Spark, NiFi, Apache Beam, Apache Airflow etc.;

-Some experience in the following Big Data frameworks: File Format (Parquet, AVRO, ORC), Resource Management, Distributed Processing;

Technical Expertise:

- Familiarity with MS Azure, Amazon Web Services (AWS), GCP or another public cloud service;

- Some experience/knowledge in at least one scripting language (Python, Perl, JavaScript);

- Some experience implementing open-source frameworks & exposure to various open source & package software architectures (AngularJS, ReactJS, Node, Elastic Search, Spark, Scala, Splunk, Apigee, and Jenkins etc.).

- Some experience with various NoSQL databases (Hive, MongoDB, Couchbase, Cassandra, and Neo4j) will be a plus

Responsibilities:

- Scale customer data science workloads and implement new pipelines;

- Work directly with data scientists, product owners and customers to integrate models with business processes;

- Deploy and operationalize ML models:

- Model hyperparameter optimization;

- Model training and automated retraining;

- Model workflows onboarding, integration, monitoring, and decommissioning;

- Model version tracking and governance;

- Model drift monitoring.

- Transform complex analytical models in scalable, production-ready solutions;

- Triage, support, resolve and automate anomaly detection for enterprise scale machine learning model deployments.

We offer:

- Competitive compensation based on your skills, experience, and customer satisfaction;

- Opportunity to work on challenging and exciting international projects;

- Flexible working hours and the possibility to work remotely when needed;

-Regular performance evaluation twice a year;

- Long-term contract with 20-25 paid time off working days (for vacation, sick, and personal leave);

- Corporate English courses from A1 to C1 level and monthly English-speaking clubs;

- Compensation of professional conference attendance according to the corporate policy;

- Compensation of medical insurance/gym according to the corporate policy;

- Casual, friendly, and family work environment, flat organizational structure;

- Regular knowledge sharing meetups and various corporate events (such as the company’s Birthday celebration and summer family party);

- Newborn and wedding bonuses;

- Gameroom with a lounge zone, PlayStation, and sports equipment;

- Travel and visa assistance for employees;

- Coffee, cookies, nuts, and dried fruits for a productive day;

- Friday brunches with fresh bakery;

- Yoga lessons;

- Secure bike storage (Odessa).