ml engineer for retail operations

Circle K operates stations offering products and services. The company also provides retail products and services through its stations.

• Design and build agentic workflows that use LLMs to plan, reason, and autonomously execute multi-step tasks
• Build and deploy agentic solutions across AI platforms and ecosystems, selecting platforms and tools for each use case
• Develop tool-use and function-calling integrations connecting agents to APIs, databases, and internal systems
• Implement orchestration patterns for complex task automation
• Build RAG pipelines to ground agent outputs in proprietary or real-time data
• Establish guardrails, evaluation harnesses, and monitoring for agent reliability, safety, and task success rates
• Prototype and iterate on agent behaviors using frameworks such as LangGraph, LlamaIndex, CrewAI, or custom orchestration layers
• Design workflows that use structured outputs from perception and detection models to trigger downstream actions, including alerts, corrective tasks, or automated reports
• Design, train, fine-tune, and evaluate machine learning and deep learning models for production use cases, including computer vision and object detection models
• Build and maintain scalable data pipelines for training, validation, and inference
• Implement MLOps practices, including model CI/CD, versioning, monitoring, and automated retraining
• Optimize model performance, latency, and cost across training and inference, including on-device or edge/mobile deployments
• Design pipelines to compare model-detected states against source-of-truth data, flag discrepancies, and feed results into downstream automation
• Improve model accuracy using field and production feedback, labeled-data pipelines, and active-learning loops
• Collaborate with product and design teams to turn business requirements into AI-powered features
• Integrate ML models and agents into production applications through robust, well-tested APIs
• Write clean, maintainable, well-documented code and contribute to internal tools and libraries
• Conduct A/B tests and offline and online evaluations to measure model and agent impact
• Align solutions with the AI Platform Architect’s AI architecture, technical standards, and long-term platform strategy
• Partner with business stakeholders to understand operational needs, define success metrics, and deliver measurable business value
• Partner with data engineers, product managers, and domain experts to define success metrics
• Communicate technical trade-offs and results clearly to technical and non-technical stakeholders
• Track emerging ML, LLM, and agentic architecture research and evaluate its applicability

• Bachelor's or master's degree in computer science, Machine Learning, or a related field, or equivalent experience
• 5+ Years of experience building and deploying ML models in production
• Strong Python programming skills and familiarity with PyTorch, TensorFlow, and scikit-learn
• Hands-on experience building and deploying agents on at least one major AI platform, with strong interest in working across multiple platforms
• Experience with LLM APIs and prompt engineering across providers, including OpenAI, Anthropic, and Google
• Familiarity with vector databases and RAG architectures
• Production experience with computer vision or image recognition models
• Solid understanding of software engineering fundamentals, including version control, testing, and API design
• Experience with AWS, GCP, or Azure
• Будет плюсом: experience with Azure AI Foundry Agent Service or Google Vertex AI Agent Builder, cross-platform agent portability, multi-agent orchestration frameworks, agentic systems combining perception outputs with reasoning and action layers, reinforcement learning or LLM fine-tuning (LoRA, RLHF), Docker and Kubernetes, open-source ML/AI contributions, LLM/agent evaluation frameworks, on-device or edge deployment optimization

• Contract of employment
• Annual bonus
• Private medical care
• Cafeteria Platform/Multisport
• Company-subsidized English lessons
• Group insurance
• Two additional days off (Good Friday and the Friday after Corpus Christi), with the possibility of exchanging them for other holidays
• Employee Referral Bonus Program
• Discounts on products and services at Circle K stations
• Employee stock purchase plan
• Employee Assistance Program (Lyra)
• Trainings and opportunities to develop skills in an international environment