Principal AI Architect

AXA•Hartford, CT

1d•Remote

About The Position

The Principal AI Architect to design and lead the technical strategy for our AI infrastructure, platforms, and systems. In this role, you will own the architectural vision for how we build, deploy, and scale AI solutions across the organization. You'll work at the intersection of AI/ML innovation, systems design, and engineering excellence, partnering with product, data science, and infrastructure teams to build robust, scalable, and responsible AI systems. This is a high-impact technical leadership role ideal for someone who is passionate about AI systems, thinks deeply about architecture and design patterns, and is excited about solving complex technical challenges at scale.

Requirements

Extensive software engineering or systems architecture experience
Moderate hands-on experience designing and building large-scale AI/ML systems
Proven track record architecting systems that have been deployed to production at scale
Experience leading technical architecture decisions on complex, mission-critical systems
Demonstrated expertise in distributed systems, scalability, and performance optimization
Deep expertise in AI/ML fundamentals, algorithms, and best practices
Outstanding understanding of modern ML frameworks and tools (TensorFlow, PyTorch, JAX, etc.)
Proficiency in at least one programming language (Python, Java, C++, Go, etc.)
Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes)
Robust understanding of data engineering, ETL pipelines, and data infrastructure
Knowledge of database systems, data warehousing, and query optimization
Familiarity with API design, microservices, and distributed system patterns
Strategic thinking with ability to balance innovation and pragmatism
Outstanding problem-solving skills with ability to break down complex technical challenges
Excellent communication skills; ability to explain complex concepts clearly
Leadership presence and ability to influence technical teams and stakeholders
Intellectual curiosity and passion for staying current with AI/ML research and trends
Comfort with ambiguity and ability to make decisions with incomplete information
Robust judgment and ability to make sound architectural trade-offs
Track record of mentoring senior engineers and technical leads
Experience navigating organizational and technical complexity at scale
Thought leadership in AI/ML architecture (speaking engagements, publications, open-source contributions)

Nice To Haves

Bachelor’s degree in Business, Computer Science, Project Management or a related field. Engineering, computer science, data science, or machine learning background
Understanding of AI infrastructure, model training, deployment, and MLOps
Familiarity with prompt engineering, fine-tuning, and LLM customization
Knowledge of RAG (Retrieval-Augmented Generation), agents, and advanced AI architectures
Experience in enterprise SaaS, B2B, or B2B2C product management
Track record in regulated industries (finance, healthcare, enterprise) or compliance-heavy environments
Previous experience with platform or ecosystem products
Demonstrated understanding of AI safety, fairness, bias mitigation, governance and responsible AI frameworks
Knowledge of AI ethics principles and ability to operationalize them in product decisions
Experience with generative AI, LLMs, or conversational AI products
Experience with large language models (LLMs), transformers, and generative AI systems
Expertise in RAG (Retrieval-Augmented Generation), fine-tuning, and prompt engineering
Experience designing AI agents and multi-step reasoning systems
Knowledge of model compression, quantization, and inference optimization
Familiarity with reinforcement learning or other advanced ML techniques
Experience designing and operating ML platforms (Kubeflow, MLflow, SageMaker, Vertex AI, etc.)
Expertise in model serving and inference optimization (TensorFlow Serving, Triton, etc.)
Experience with feature stores and feature engineering platforms
Knowledge of monitoring, observability, and alerting for ML systems
Experience with infrastructure-as-code and CI/CD pipelines for ML

Responsibilities

Own the end-to-end technical architecture for AI systems, platforms, and infrastructure
Design scalable, modular, and extensible AI architectures that support multiple use cases
Define technical standards, best practices, and design patterns for AI development across the organization
Evaluate and recommend AI/ML frameworks, tools, and technologies (LLMs, vector databases, orchestration platforms, etc.)
Design solutions for complex challenges: model serving, real-time inference, batch processing, multi-model systems
Create architecture documentation, diagrams, and technical specifications for cross-functional teams
Conduct architecture reviews and provide technical guidance on design decisions
Design and architect AI/ML platforms and infrastructure for scale (training, inference, monitoring, deployment)
Define MLOps and model lifecycle management practices (versioning, governance, lineage, reproducibility)
Design data pipelines, feature engineering infrastructure, and data management systems
Architect solutions for model serving, inference optimization, and latency requirements
Plan and execute infrastructure upgrades, migrations, and technical debt reduction
Establish monitoring, observability, and alerting frameworks for AI systems
Work with DevOps and Infrastructure teams to operationalize AI systems
Stay at the forefront of AI/ML research and emerging technologies
Evaluate new models, frameworks, and techniques for strategic relevance and business impact
Design proof-of-concepts and pilots for emerging AI technologies (generative AI, multimodal models, agents, etc.)
Provide technical thought leadership on AI strategy and technology roadmap
Mentor data scientists and engineers on architectural best practices and design patterns
Contribute to technical strategy discussions with product and business leadership
Lead and mentor senior engineers, architects, and technical leads
Establish technical vision and roadmap aligned with business objectives
Drive technical decision-making and architecture governance across AI teams
Partner with Product to translate business requirements into technical architecture
Collaborate with Data Science, Engineering, and Infrastructure teams on design and implementation
Lead design reviews, architecture discussions, and technical problem-solving sessions
Build and maintain robust relationships with key technical stakeholders
Design systems and processes to ensure responsible AI practices (bias detection, fairness, explainability)
Establish governance frameworks for model performance, safety, and ethical deployment
Design monitoring and alerting for model drift, performance degradation, and fairness metrics
Navigate regulatory requirements and ensure compliance with AI regulations
Architect solutions for model interpretability, transparency, and auditability
Lead technical discussions on AI ethics, safety, and responsible system design