ml engineer for retail operations

HireHi; Warszawa; On-site; PLN 180,000 - 260,000; Full time

Benefits offered by this job

Annual bonus

Medical care

Cafeteria

English lessons

Group insurance

Extra days off

Referral bonus

Stock plan

Assistance program

Training

Job summary

HireHi ищет инженера ML с опытом разработки и развёртывания продакшн-моделей на базе PyTorch, TensorFlow и scikit-learn. В ваши задачи входит создание агентских рабочих процессов, интеграция решений с API, базами данных и внутренними системами, а также настройка MLOps и мониторинга.

Кандидат будет работать над продолжением и расширением возможностей ИИ‑платформ в много-платформенной среде, участвуя в проектировании и тестировании сложных сценариев с перцепцией и реакцией на данные.

Qualifications

• Образование: бакалавр или магистр в области CS/ML или эквивалентный опыт.
• Опыт построения и развёртывания ML-моделей в продакшене 5+ лет.
• Сильные навыки Python и опыт работы с PyTorch, TensorFlow и scikit-learn.

Responsibilities

• Проектирование и сборка агентских рабочих процессов с использованием LLMs.
• Разработка агентных решений на платформах AI и интеграция с API/базами данных.
• Настройка конвейеров MLOps, CI/CD и мониторинга.
• Интеграция агентных систем с perception outputs и поясняющими слоями.

Skills

Python

PyTorch

TensorFlow

scikit-learn

LLM APIs

Prompt engineering

Vector databases

RAG architectures

MLOps

Docker

Kubernetes

APIs

Version control

Education

Bachelor’s or Master’s in CS/ML

Tools

Azure AI Foundry

Vertex AI Agent Builder

Docker

Kubernetes

Job description

Описание:

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

Задачи:

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

Требования:

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

Условия:

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