AI Engineer (Solutions Architect + Applied AI)

📋 Description

• • As an AI Engineer (Solutions Architect + Applied AI) at Humanintelligence Inc., you will be at the forefront of designing, building, and operating the company’s cutting-edge AI-first technology platforms. This role is a unique blend of deep, hands-on engineering expertise and high-level architectural ownership, empowering you to shape the future of our AI ecosystem.
• • You will be instrumental in developing scalable, production-grade AI systems that are the backbone of our Product and wellness technology platforms. This is not a role focused solely on experimental machine learning; instead, it is dedicated to ensuring our AI infrastructure is robust, secure, reliable, and delivers tangible, real-world impact.
• • A significant emphasis of this position will be on agentic AI development. You will architect and manage AI agent systems and intuitive agent-builder platforms, enabling non-programmers across the organization to contribute to technology development without requiring them to learn traditional software engineering.
• • Your responsibilities will span the entire AI lifecycle, from conceptualizing the architecture and guiding implementation to ensuring the seamless operation of the AI platform, from data pipelines to model deployment, all while adhering to production-grade reliability and responsible AI standards.
• • You will collaborate closely with leadership, product, clinical, and engineering teams, translating ambitious product visions into functional, production-ready AI systems.
• • AI Platform Architecture and Infrastructure:
• • Design, implement, and maintain a scalable, AI-first architecture tailored for multi-tenant B2B2C platforms, robust APIs, and flexible white-label deployments.
• • Construct and manage event-driven systems, modern data infrastructure, and distributed service architectures that form the foundation of our AI capabilities.
• • Work extensively with Azure managed cloud services, leveraging serverless infrastructure and containerized workloads to build efficient and adaptable solutions.
• • Oversee and manage critical infrastructure components, including Vector Databases, Feature Stores, Data Pipelines, CI/CD Pipelines, Infrastructure-as-Code (IaC) implementations, and robust Secrets and Identity Management systems.
• • Establish and enforce stringent operational standards, including Service Level Indicators (SLIs), Service Level Objectives (SLOs), error budgets, comprehensive monitoring, proactive alerting, and detailed incident runbooks.
• • Architect infrastructure with a keen focus on cost-efficiency, scalability, and unwavering reliability as primary design principles.
• • Utilize AI-assisted engineering workflows to accelerate the architecture design process, streamline infrastructure provisioning, and enhance system documentation.
• • Applied AI and Machine Learning Systems:
• • Translate complex product and clinical use cases into tangible, production-ready AI features and powerful model-enabled capabilities.
• • Develop sophisticated systems incorporating Retrieval-Augmented Generation (RAG), advanced AI agents and tool-use systems, cutting-edge Multimodal AI applications, and insightful Time-series analysis on wearable data.
• • Manage the complete model lifecycle, encompassing robust Model Evaluation Frameworks, meticulous Prompt Engineering and Prompt Versioning, efficient Model Versioning and Experimentation, rigorous Offline and Online A/B Testing, and Continuous Model Improvement Pipelines.
• • Implement resilient pipelines for data labeling, weak supervision techniques, retrieval optimization, and continuous performance monitoring.
• • Maintain a deep and current understanding of modern AI orchestration tools, including LangChain, and leading Large Language Models (LLMs) providers such as GPT, Claude, Gemini, and Grok.
• • Agentic AI and AI Driven Development:
• • Lead the development of advanced agentic AI systems and user-friendly agent-builder platforms, empowering stakeholders across the company to actively participate in technology creation.
• • Develop innovative AI-driven workflows that facilitate AI-assisted coding, automate complex development tasks through agents, and enable AI-assisted system configuration and infrastructure deployment.
• • Employ modern AI engineering methodologies to significantly accelerate build cycles, reduce manual development overhead, and enhance overall engineering velocity.
• • Contribute to the evolution of AI-enabled Software Development Lifecycles (SDLCs), including AI-assisted requirement interpretation, automated test generation, efficient regression testing automation, and streamlined release validation and deployment automation.
• • Data Governance, Privacy, and Security:
• • Design systems with a strong emphasis on privacy-by-design principles, particularly for handling sensitive health and personal data.
• • Define and implement clear policies for Personally Identifiable Information (PII) and Protected Health Information (PHI) handling, robust consent management, detailed data lineage and traceability, effective retention policies, and compliance with cross-border data regulations.
• • Support seamless integrations with critical external systems, such as Wearables platforms, Electronic Health Records (EHR), Laboratory Information Systems (LIS), and Payment systems.
• • Ensure all developed systems maintain a strong security foundation, incorporating encryption, secure key management, and adherence to least-privilege access controls.