Platform Architect - AI

Circle K; Województwo pomorskie; Vor Ort; PLN 260.000 - 380.000; Vollzeit

Zusammenfassung

Circle K is seeking an experienced Platform Architect (AI) to design and scale AI-driven solutions, including a visual similarity search pipeline for store display verification. You will define end-to-end architectures, select technologies, and ensure secure, compliant deployments across data infra and third‑party integrations.

You will mentor engineers, oversee MLOps practices, and stay current with foundation models and vector databases to inform CK challenges.

Qualifikationen

• 5+ years of software architecture experience, including AI/ML systems
• 2–3 years focused on AI/ML architectures and delivery
• Strong understanding of ML fundamentals, deep learning, and NLP/LLMs
• Hands-on experience with cloud AI/ML services (AWS/Azure/GCP)
• Experience with MLOps tools and CI/CD for ML pipelines
• Proficiency in Python and popular ML frameworks (PyTorch, TensorFlow, Hugging Face)
• Experience with vector databases, RAG pipelines, and multi-signal retrieval
• Strong data architecture, APIs, and system integration skills
• Excellent communication to explain complex tech to non-technical stakeholders
• Experience leading technical teams or mentoring engineers

Aufgaben

• Design end-to-end AI/ML platform architectures from concept to production
• Select technologies, frameworks, and models for scalable solutions
• Oversee integration with data infra and existing systems
• Evaluate build vs. buy and model cost vs. risk
• Ensure scalable, secure, GDPR-compliant AI architectures
• Lead MLOps/LLMOps practices and monitoring strategies
• Mentor engineers on architecture patterns and best practices
• Stay current with AI models and vector databases and assess applicability
• Create docs, diagrams, and decision records
• Coordinate with AI Security for controls and data protection
• Oversee API contracts and model outputs for downstream systems
• Define quality thresholds for product category AI signals
• Review licenses for open-source components and ensure compliance
• Provide architecture oversight rather than day-to-day coding
• Plan feedback loops from field data into index and retraining

Kenntnisse

Software architecture

AI/ML systems

Cloud platforms

MLOps

Python

PyTorch

TensorFlow

Hugging Face

NLP/LLM

Leadership

Communication

APIs

Vector databases

RAG

Tools

MLflow

Kubeflow

SageMaker

Pinecone

Jobbeschreibung

Job Description

We’re looking for an experienced Platform Architect (AI) to design, implement, and scale AI-driven solutions across our organization. This role combines deep technical expertise with strategic thinking, translating business needs into concrete, production-ready AI architectures. The work spans several domains and technology stacks - computer vision, language models, document processing, and data-driven automation. Each project will bring its own constraints, its own trade-offs, and often its own toolset.

The first project in scope is a visual similarity search/embeddings/vector retrieval solution for automated verification of store display compliance.

Responsibilities:

• Design end-to-end architecture for AI/ML systems, from proof of concept to production

• Select appropriate technologies, frameworks, and models

• Oversee integration of AI solutions with existing systems and data infrastructure

• Evaluate build vs. buy decisions for AI capabilities, including cost modelling and vendor lock-in risk

• Ensure solutions are scalable, secure, cost-efficient, and compliant with relevant regulations (e.g. GDPR, EU AI Act)

• Guide MLOps/LLMOps practices - retraining triggers, drift and quality monitoring criteria, versioning strategy across models and data, and rollback paths and work with MLOps engineers on their implementation

• Mentor AI Engineers on AI architecture patterns and emerging best practices

• Stay current with the AI landscape (foundation models, vector databases, agentic frameworks, RAG, fine-tuning) and assess their applicability to CK challenges

• Create and maintain technical documentation, architecture diagrams, and decision records

• Act as the technical liaison to the organization’s AI Security function - bringing them in at key design decisions (data flows, model endpoints, third-party integrations) rather than making security calls unilaterally; ensure access control, encryption, and safeguards against adversarial or malicious inputs (e.g. spoofed images, injection attacks on NL components) meet organizational standards, and verify new third‑party integrations against existing security approvals rather than assuming blanket coverage from prior vendor vetting.

Project‑specific requirements (visual similarity search):

• Design and maintain the pipeline architecture: product detection - parallel analysis (visual embedding, OCR, category classification) - hybrid query to the vector database - matching against the planogram

• Decide on specific models and components (e.g. RF‑DETR vs. alternatives, DINOv2 as embedder), weighing trade‑offs: accuracy, cost, licensing, time to delivery

• Design category‑specific query strategies (e.g. different signal weights for cigarettes vs. bottles - text‑signal dominance vs. visual‑embedding dominance)

• Make hosting and cost decisions: self‑hosted GPU infrastructure (AWS) vs. managed services (Roboflow Inference, Pinecone SaaS), appropriate to the scale

• Define and oversee the API contract between the AI pipeline and the rest of the system (backend, RELEX integration) - what exactly the model returns, how low‑confidence matches are handled

• Set measurable quality thresholds (precision/recall per product category, acceptable false‑positive rate) jointly with the Product Owner

• Verify license compliance of chosen components (e.g. Apache vs. AGPL‑3.0 for YOLO‑family models) against the product’s business model

• Provide architectural oversight of the AI/ML Engineer’s work - architectural code review, not day‑to‑day implementation

• Plan the improvement loop: how approved image crops from field verification feed back into the index as additional real‑world reference material

• Engage the organization’s AI Security team early to review the pipeline’s data flows (store photos, embeddings, RELEX and Pinecone connections) and confirm required controls; verify the RELEX integration follows existing organizational security standards for this specific access pattern (e.g. real‑time API vs. batch export), and work with AI Security to define requirements for new components introduced by this project - encryption in transit and at rest, access control for the Pinecone connection, and a data‑retention policy for photos that may incidentally capture employees or customers (GDPR relevance given the Polish/EU context).

Requirements:

• 5+ years of experience in software architecture, with at least 2–3 years focused on AI/ML systems

• Strong understanding of machine learning fundamentals, deep learning, and modern NLP/LLM architectures

• Hands‑on experience with cloud platforms (AWS, Azure, or GCP) and their AI/ML services

• Experience with MLOps tools (e.g., MLflow, Kubeflow, SageMaker) and CI/CD for ML pipelines

• Proficiency in Python and familiarity with ML frameworks (PyTorch, TensorFlow, Hugging Face)

• Experience with vector databases, RAG pipelines, and agentic AI frameworks

• Solid grasp of data architecture, APIs, and system integration patterns

• Excellent communication skills - able to explain complex technical concepts to non‑technical stakeholders

• Experience leading technical teams or mentoring engineers.

Additional project‑specific (visual similarity search) requirements:

• Hands‑on experience with production computer vision pipelines (not just research projects) - object detection, embedding models, vector/hybrid search

• Familiarity with the ecosystem: DETR/YOLO‑family models, Roboflow (or a comparable data management and training tool), vector databases (Pinecone or equivalent)

• Ability to evaluate cost trade‑offs between self‑hosted GPU and managed/SaaS inference

• Understanding of open‑source model licensing constraints (AGPL, Apache, MIT) and their business implications

• Experience designing systems with multiple parallel decision signals (multi‑signal retrieval/ranking)

• Working knowledge of AWS (ML/GPU area: SageMaker, EC2 GPU, Bedrock) - doesn’t need to be a DevOps expert, but must be able to discuss architecture with the DevOps Engineer