Principal AI Architect

Accountabilities:

• • Define technical strategy and lead the end-to-end architecture of complex systems spanning cloud infrastructure, data platforms, AI/ML workloads, and enterprise applications.
  • Architect scalable cloud-native, multi-cloud, and hybrid solutions using technologies such as AWS, Azure, GCP, Kubernetes, containers, serverless platforms, and event-driven architectures.
  • Design robust data architectures, including data warehouses, data lakes, batch and streaming pipelines, data models, and orchestration workflows.
  • Lead the design of production AI and machine learning systems, including LLM-based applications, agentic systems, model serving, MLOps pipelines, feature stores, evaluation frameworks, and responsible AI practices.
  • Establish standards for infrastructure as code, CI/CD, DevOps, observability, reliability, and operational excellence across technical engagements.
  • Drive architectural decisions that optimize system performance, scalability, cost efficiency, security, and long-term maintainability.
  • Lead cloud migration, modernization, and platform transformation initiatives while ensuring architecture aligns with business and product objectives.
  • Ensure solutions meet applicable security, governance, privacy, and compliance requirements, including frameworks such as GDPR, HIPAA, and SOC 2 where relevant.
  • Serve as the senior technical voice in discussions with leadership and clients, translating complex technical concepts into clear recommendations for technical and non-technical stakeholders.
  • Partner closely with engineering, data, AI, and product teams to align architecture, delivery priorities, and implementation plans.
  • Develop and maintain architecture documentation, technical standards, design guidelines, and reusable best practices.
  • Mentor experienced engineers and architects, helping strengthen technical judgment, leadership capabilities, and overall engineering impact.
  • Own the most complex architectural and integration challenges, making high-stakes technical decisions and resolving critical system design issues.
  • Evaluate emerging technologies, frameworks, and AI tools, recommending practical approaches that improve delivery quality, speed, and technical outcomes.
  • Apply an AI-forward mindset by leveraging modern AI-assisted development tools and practices to improve productivity, experimentation, and engineering quality.
  • Support client engagements that may require up to 20% travel within the United States.

  Requirements:

  • 8+ years of experience in software engineering, with at least 5 years in technical leadership, architecture, or other senior engineering roles.
  • Deep expertise in cloud platforms and cloud-native architecture, with experience across AWS, Azure, GCP, or comparable environments.
  • Strong background designing distributed systems using microservices, serverless technologies, containers, Kubernetes, Docker, and event-driven architectures.
  • Proven experience with infrastructure as code and CI/CD practices using tools such as Terraform, CloudFormation, Pulumi, GitHub Actions, or similar technologies.
  • Extensive data architecture experience across relational, NoSQL, warehouse, and big data environments, including technologies such as PostgreSQL, MongoDB, Snowflake, BigQuery, Spark, and Kafka.
  • Hands-on experience with data modeling, ETL/ELT pipelines, streaming architectures, and orchestration platforms such as Airflow, Prefect, or dbt.
  • Strong experience designing and delivering production AI/ML systems, including LLM applications, MLOps, model serving, model evaluation, and agentic or multi-agent architectures.
  • Familiarity with AI/ML technologies and platforms such as SageMaker, Vertex AI, MLflow, Hugging Face, PyTorch, TensorFlow, or similar tools.
  • Strong software engineering capabilities, particularly with Python, APIs, distributed services, and containerized applications.
  • Solid understanding of cloud networking, security, identity and access management, governance, observability, and compliance frameworks.
  • Demonstrated experience leading complex technical initiatives and mentoring senior engineers or architects.
  • Excellent stakeholder management and communication skills, with the ability to explain technical concepts and architectural tradeoffs across diverse audiences.
  • Strong problem-solving skills and technical judgment, with the ability to make decisions effectively in ambiguous and rapidly evolving environments.
  • Demonstrable experience using modern AI-assisted development tools to accelerate engineering and improve delivery quality.
  • Multi-cloud architecture experience, responsible AI expertise, enterprise architecture certifications, or relevant AWS, Azure, GCP, or TOGAF certifications are considered advantageous.
  • A proactive, resourceful, and ownership-driven mindset, with a commitment to continuous learning, direct communication, collaboration, and high-quality execution.

  Benefits:

  • Salary: $165,604–$228,491 USD, with final compensation determined based on factors such as experience, skills, qualifications, certifications, seniority, geographic location, and business needs.
  • Comprehensive healthcare coverage: Medical, dental, and vision insurance for eligible employees.
  • Retirement benefits: 401(k) plan.
  • Paid time off: PTO designed to support rest, personal time, and work-life balance.
  • Professional growth: Opportunities to work on complex cloud, data, and AI initiatives while collaborating with highly experienced technical professionals.
  • Innovative work environment: Exposure to modern AI technologies, production-ready AI systems, emerging tools, and challenging enterprise architecture problems.
  • Leadership impact: The opportunity to influence technical strategy, architectural standards, engineering practices, and the development of experienced technical talent.
  • Flexible and collaborative culture: A high-ownership environment focused on creativity, practical problem-solving, experimentation, and delivering measurable outcomes.
  • Travel opportunities: Occasional travel within the United States, with an expected travel requirement of up to 20%.