Senior AI Solutions Architect

Coaction Global • Morristown, NJ

2d • $180,000 - $230,000

About The Position

At Coaction, we’re a unique mix of leaders, achievers, thinkers, and team players with a high-performance mindset and a diverse skillset. We bring our industry expertise together to continually push the boundaries of what insurance can be for our clients. At Coaction, we're a unique mix of leaders, achievers, thinkers, and team players with a high-performance mindset and a diverse skillset. We bring our industry expertise together to continually push the boundaries of what insurance can be for our clients. At Coaction, innovation is key to our continued success and growth. As we expand our digital and data capabilities, Artificial Intelligence will play a critical role in enhancing underwriting, operations, and customer experience. We believe the best solutions happen when product, engineering, data, security, and the business partner as one integrated team -open to ideas, ready to challenge the status quo, and focused on delivering measurable outcomes in a regulated industry. This is our first dedicated Senior AI Solutions Architect role. This role will be instrumental in defining the technical vision and architecture for how we design, build, deploy, and operate AI-powered solutions safely and responsibly across the enterprise. The ideal candidate is a pragmatic, hands-on architect with an extensive track record of senior technical leadership—focused on translating business strategy into scalable, secure, production-grade AI architecture (not research-only)—and comfortable establishing the repeatable patterns, reference architectures, and guardrails that engineering teams will build on. This role carries a high degree of autonomy and is expected to shape AI strategy at the executive level, not just execute against it.

Requirements

• 15+ years of software engineering/architecture experience, including designing and delivering production systems (APIs, services, data pipelines, or enterprise integrations) at scale.
• Bachelor’s or master’s degree in computer science, Information Systems, or a related field, or equivalent professional experience.
• Extensive experience as a senior solutions or technical architect, defining reference architectures and technical standards adopted by multiple engineering teams.
• Strong proficiency in at least one production language such as Python, C#, Java, or similar, with the ability to prototype and validate architectural decisions hands-on.
• Deep experience architecting integrations with third-party APIs and services; strong understanding of distributed systems, REST/JSON, Enterprise integration patterns, and authentication/authorization patterns.
• Proven hands-on experience architecting LLM-based solutions (prompting, structured outputs, tool/function calling) and/or ML-enabled services, ideally across multiple production deployments.
• Strong knowledge of RAG architecture (embeddings, vector search, grounding/citations); experience with vector databases or enterprise search platforms is a plus.
• Deep understanding of LLMOps/MLOps practices: versioning, evaluation, monitoring, incident response, and change control, and how to architect for them.
• Hands-on experience with MLOps frameworks and tooling (e.g., MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI, or similar) for model/pipeline versioning, experiment tracking, and automated retraining/deployment.
• Strong database and data engineering skills, including relational and NoSQL data stores, data modeling, ETL/ELT pipeline design, and building scalable, reliable data pipelines to support AI/ML and analytics workloads.
• Experience with cloud platforms (AWS and/or Azure) and production operations (secrets management, logging/monitoring, cost controls) at an architectural level.
• Strong engineering hygiene: source control, code review, automated testing, CI/CD, and performance monitoring, with the ability to set standards for others.
• Strong understanding of AI risk and security considerations (prompt injection, data privacy/PII handling, vendor/model risk management) in regulated environments.
• Excellent verbal and written communication skills; proven ability to influence senior stakeholders and translate business needs into technical architecture and roadmaps.

Nice To Haves

• Experience establishing AI governance frameworks or leading AI strategy conversations with executive stakeholders is highly valued.

Responsibilities

• Define and own the enterprise AI reference architecture, including patterns for LLM/ML integration, RAG pipelines, and Agentic AI platforms, ensuring alignment with enterprise architecture and security standards.
• Architect secure integration patterns for external AI services (LLMs and ML APIs), including data handling, access controls, and auditability, and guide engineering teams in implementing them.
• Design retrieval-augmented generation (RAG) architectures for document-heavy workflows (e.g., submissions, endorsements, claims documentation) that scale across multiple enterprise data sources.
• Lead the design and rollout of an Agentic AI platform reference architecture, partnering with Architecture and Engineering on implementation.
• Define and govern reusable AI components and platform building blocks that accelerate delivery across multiple use cases.
• Partner with architecture, engineering, data, and business stakeholders—including senior leadership—to translate strategic needs into solution designs, technical requirements, and delivery roadmaps.
• Establish enterprise-wide AI engineering standards for testing and evaluation (quality, safety, regression testing), and guide their integration into CI/CD pipelines.
• Define the architecture for monitoring and observability of AI solutions (latency, cost, quality signals, guardrail events) and establish operational runbook standards.
• Lead AI governance efforts including vendor/model intake, risk reviews, architecture documentation, and change management processes.
• Set security architecture standards for AI systems, including safeguards against prompt injection, data leakage, and misuse.
• Author architecture decision records, reference designs, and technical documentation; lead knowledge transfer to broaden internal capability and reduce reliance on external partners over time.
• Mentor engineers and junior architects on AI engineering best practices, and serve as the internal escalation point for complex architectural decisions.
• Stay ahead of AI engineering tools, platforms, and architectural patterns; advise leadership on pragmatic adoption aligned to business value and risk posture.

Benefits

• paid time off
• medical
• dental
• retirement

What This Job Offers

• Job Type: Full-time
• Career Level: Senior