Вакансия AI Architect

Skills

• LLM
• RAG
• Rest API
• AWS
• GCP
• Azure
• Docker
• Kubernetes

Описание вакансии

What you will do

• Architect & Roadmap: Lead design sessions and produce detailed AI system blueprints, from data ingestion to inference services;
• Agentic Pipelines: Specify and oversee construction of end-to-end LLM/ML pipelines for structured and unstructured energy data;
• RAG & Vector Search: Define strategies for retrieval-augmented generation, vector databases, and knowledge graphs to power contract intelligence;
• Model Development: Guide teams building models for contract auditing, price forecasting, and portfolio optimization;
• ML Pipeline to Full-Stack Deploy: Design and oversee the end-to-end deployment of machine learning and AI pipelines into production, covering service hosting, containerization, and cloud infrastructure;
• Standards & Best Practices: Establish coding standards, testing frameworks, and review processes for AI/ML workflows;
• Mentorship & Leadership: Coach engineers in applied AI, review design docs, and present architecture reviews to stakeholders, including C-suite.

Must haves

• 8+ years of professional software development, with 5+ years in production ML/AI systems;
• Proven track record as an AI Architect or technical lead, able to envision complex systems and drive them to production;
• Demonstrated experience in AI research & development;
• Publishing, prototyping novel approaches, or leading R&D projects;
• Proven experience building with LLMs (locally hosted and API-based), plus hands-on RAG/vector search experience;
• Fluent in designing microservices and REST APIs for AI inference at scale;
• Strong systems-level thinking and product mindset, able to balance research and engineering trade-offs;
• Experience with cloud-native platforms (AWS, GCP, Azure), Docker, and Kubernetes;
• Excellent verbal and written communication;
• Сomfortable presenting to executive audiences;
• Upper-Intermediate English level.

Nice to haves

• Early-stage startup experience, especially shaping nascent AI products;
• Domain experience in energy, sustainability, or industrial infrastructure;
• Thought-leadership in AI/ML: open-source contributions, publications, or conference talks;
• Commitment to ethical, socially responsible AI.