Mid-Senior Machine Learning Engineer @ Harvey Nash Technology in Harvey Nash Technology

  Technologies Utilized

• Programming Languages: Python, Go 

• Machine Learning Frameworks: TensorFlow, PyTorch 

• Cloud Platforms: AWS 

• Big Data
  Tools: Spark, Snowflake, Snowpark 

• CI/CD and Orchestration
  Tools: Github Actions, Airflow 

• Monitoring
  Tools: Grafana 

Skills and Qualifications

• Education: Degree in Computer Science or related field. 

• Experience: Minimum of 2 years of proven industry experience. 

• Programming Skills: Proficient in Python or other object-oriented programming languages. 

• Strong understanding of data structures, algorithms, and software engineering principles. 

• Knowledge of mainstream ML libraries (e.g., TensorFlow, PyTorch, Spark ML) and/or cloud solutions (e.g., AWS, Sagemaker). 

• Familiarity with CI/CD (e.g., Github Actions, Airflow) and big data tools (e.g., MapReduce, Spark, Flink, Kafka, Docker, Kubernetes). 

• Database Skills: Experience in SQL and database management, including SQL query optimization. 

• Testing Expertise: Experience with unit testing frameworks. 

  Technologies Utilized

• Programming Languages: Python, Go 

• Machine Learning Frameworks: TensorFlow, PyTorch 

• Cloud Platforms: AWS 

• Big Data
  Tools: Spark, Snowflake, Snowpark 

• CI/CD and Orchestration
  Tools: Github Actions, Airflow 

• Monitoring
  Tools: Grafana 

,[ Develop, test, and deploy scalable, low-latency machine learning solutions and pipelines., Research and explore the latest advancements in machine learning platform technologies, pushing the limits of what is achievable with ML, while staying current with industry trends and developments. , Experiment with and prototype new ML platforms tailored to specific environments, creating rapid prototypes and proof-of-concepts. , Automate ML pipelines using CI/CD principles, promoting consistency and agility across the development lifecycle. , Conduct thorough testing to identify and resolve potential issues, including bias or fairness concerns. , Optimize model deployment processes, including unit, integration, and stress testing, ensuring high engineering quality. , Design and build the next-generation machine learning infrastructure to support the simultaneous operation of thousands of model training pipelines and billions of daily batch predictions. , Work closely with internal ML teams (such as Data Scientists and MLOps teams) to enhance codebase quality and overall product health. ]

Requirements: Python, Object-oriented programming, Data structures, Cloud, Big Data, SQL, Unit testing, TensorFlow, PyTorch, Spark, AWS, GitHub Actions, Airflow, Flink, Kafka, Kubernetes, Snowflake
Tools: .
Additionally: Sport subscription, Training budget, Private healthcare, Lunchcard, International projects, Free coffee, Canteen, Modern office, No dress code.