Senior AI Architect Role at AgileEngine We are seeking a seasoned Senior AI Architect to lead the design and implementation of cutting-edge AI systems. As part of our team, you will be responsible for creating detailed blueprints, specifying end-to-end pipelines, and guiding teams in model development and deployment. Key Responsibilities: - Lead design sessions and produce detailed AI system blueprints from data ingestion to inference services. - Specify and oversee construction of end-to-end LLM/ML pipelines for structured and unstructured energy data. - Define strategies for retrieval-augmented generation, vector databases, and knowledge graphs to power contract intelligence. - Guide teams building models for contract auditing, price forecasting, and portfolio optimization. - Design and oversee the end-to-end deployment of machine learning and AI pipelines into production, covering service hosting, containerization, and cloud infrastructure. - Establish coding standards, testing frameworks, and review processes for AI/ML workflows. - Coch engineer engineers in applied AI, review design docs, and present architecture reviews to stakeholders, including C-suite. Requirements: - 8+ years of professional software development experience, 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, comfortable 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. Benefits: - Professional growth through mentorship, TechTalks, and personalized growth roadmaps. - Competitive compensation and budgets for education, fitness, and team activities. - A selection of exciting projects with modern solutions development and top-tier clients. - Flextime for an optimal work-life balance, working from home or going to the office.