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Everseen is a leader in vision AI. We are transforming business operations for global retailers, driving measurable business value and improving the customer experience. We are a dedicated team of inventors, research scientists, engineers, AI experts and retail industry veterans. Our mission is to protect people, process, products and profitability within the retail sector and beyond. We are trusted by major food, drug, mass, and specialty retailers around the world— including Kroger, Meijer, and Woolworths—we also partner with leading hardware, AI, and cloud computing providers such as Google, NVIDIA, NCR, and Dell. We are operationalizing vision AI at an unprecedented scale with the largest global footprint of edge AI powered Computer Vision in Retail.
Job Responsibility:
Shares skills, knowledge, and expertise with members of the data engineering team
Fosters a culture of collaboration and continuous learning by organizing training sessions, workshops, and knowledge-sharing sessions
Collaborates and drive progress with cross-functional teams to design and develop new features and functionalities
Ensure that the developed solutions meet project objectives and enhance user experience
Have influence over the technology stack and internal technical improvements, contributing to strategic decision-making
Based on requirements and a longer-term product and feature strategy, design and implement reusable, testable, efficient, and elegant code
Ensure adherence to coding standards and best practices
Creates, maintains, and runs unit tests for new and existing applications and services
Aims to deliver defect-free and well-tested solutions
Analyzes and collect data from various sources such as log files, application stack traces, and thread dumps
Utilizes data analysis to identify trends, patterns, and potential areas for improvement
Based on this, begin to implement changes
Creates and maintains CI/CD integration using various tools
Automates the build, test, and deployment processes to ensure efficiency and reliability
Researches and proposes third-party software solutions to optimize system performance
Expands product capabilities by integrating compatible third-party solutions
Monitors update and tracking of third-party solutions' compatibility with Everseen stack according to internal development guidelines
Monitors production logs to identify and troubleshoot issues promptly
Ensures seamless operation and timely resolution of any anomalies to maintain system reliability
Responsible for creating, reviewing, and maintaining high-quality technical documentation to ensure clarity, consistency, and knowledge sharing within the development team
Requirements:
4-5 years of work experience in a relevant role and global SaaS company
Bachelors degree or equivalent focusing on the computer science field is preferred
Excellent communication and cross-functional collaboration skills
Comfort working in ambiguous and fast-evolving environments
Expert knowledge of Python
Experience with CI/CD tools (e.g., GitLab, Jenkins)
Hands-on experience with Kubernetes, Docker, and cloud services
Understanding of ML training pipelines, data lifecycle, and model serving concepts
A demonstrated understanding of the ML lifecycle, model versioning, and monitoring
Experience with ML frameworks (e.g., TensorFlow, PyTorch)
Experience with GPU orchestration (e.g., NVIDIA GPU Operator, MIG)
Experience with Infrastructure as Code (e.g., Terraform)
Experience with Data engineering tools (e.g., Snowflake, Databricks, BigQuery, Airbyte, Kafka)
Familiarity with feature stores and model registries
Exposure to large-scale distributed systems and performance optimisation
Ability to work with Linux systems, including troubleshooting skills such as log investigations, performance testing, and connectivity investigation
Possesses a deep understanding of technical concepts and terminology relevant to Everseen's products and services
Expert knowledge of advanced concepts like microservices and distributed systems
In-depth knowledge of Azure Kubernetes Services for container orchestration, Azure Blob Storage for data storage, and ElasticSearch for search and analytics
Ability to leverage cloud computing technologies and services for testing and validation purposes
In-depth knowledge of cloud security, scalability, and performance optimization principles
Excellent understanding of cloud computing technologies and services, including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS)
Broad understanding of the software engineering and architecture space, including knowledge of various programming languages, frameworks, techniques, and industry trends in AI