AI Architect (Platform Architect)

Quick Facts

• Role: AI Architect (Platform Architect)
• Engagement: Full-time
• Work style: Hybrid (2–3 days per week)

Description

You will design and scale production-ready AI/ML architectures across the organization, translating business needs into secure and compliant solutions spanning computer vision, LLMs, document processing, and data-driven automation.

The initial focus is a visual similarity search system for automated in-store display compliance verification against planograms.

Responsibilities

• Architect end-to-end AI/ML solutions from PoC to production, selecting models, frameworks, and tools
• Run build-vs-buy analysis including cost modeling and vendor lock-in risk
• Define and enforce MLOps/LLMOps standards (retraining triggers, drift/quality monitoring, versioning, rollback)
• Integrate AI components with existing systems and data infrastructure
• Lead AI security design considerations (access control, encryption, protection against adversarial inputs and prompt injection)
• Design the visual similarity search pipeline: detection, embeddings, OCR, hybrid vector query, and planogram matching
• Select model/components based on accuracy, cost, licensing, and delivery time; define retrieval strategies by category
• Decide on self-hosted GPU vs managed inference (e.g., Roboflow Inference, Pinecone)
• Define API contracts and integration behavior with backend systems (including low-confidence handling)
• Set quality thresholds and design a feedback loop using field-verified image crops to improve the index
• Provide architectural code reviews, mentor engineers, and maintain architecture documentation and decision records
• Track emerging AI technologies (foundation models, vector databases, agentic frameworks, RAG, fine-tuning) and assess their fit

Requirements

• 5+ years of software architecture experience, including 2–3 years in AI/ML systems
• Strong knowledge of machine learning, deep learning, and LLM architectures
• Hands-on experience with production computer vision pipelines (object detection, embeddings, vector/hybrid search)
• Familiarity with DETR/YOLO-family models and Roboflow or similar tooling
• Experience with vector databases (Pinecone or equivalent), RAG, and agentic frameworks
• Experience designing multi-signal retrieval or ranking systems
• Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face)
• Experience with MLOps tools (MLflow, Kubeflow, SageMaker) and CI/CD for ML
• Working knowledge of AWS ML/GPU services (SageMaker, EC2 GPU, Bedrock)
• Ability to evaluate self-hosted vs managed inference costs
• Understanding of open-source licensing (AGPL, Apache, MIT) and business impact
• Pragmatic decision-making under time and budget constraints
• Experience leading or mentoring engineering teams
• Excellent communication skills with non-technical stakeholders

Benefits

• B2B contract
• Fully remote service delivery
• Budget for skills development and certifications
• Regular collaboration reviews and discussion of project scope
• Additional collaboration benefits, including networking and team-building events