Divisional Artificial Intelligence Engineer III

Alliant; United States; Remote; USD 140,000 - 210,000; Full time

Job summary

Alliant is seeking a Senior Platform Architect to own the architecture and operational health of a major AI platform domain, including agent lifecycle, sandbox context exchange, and model orchestration.

You will translate ambiguous needs into durable services and APIs, drive reliability and cost improvements, and mentor engineers across teams while shaping scalable platform patterns for multiple products.

Qualifications

• Bachelor's degree in Computer Science or related field
• 5+ years of software engineering experience, ownership of production services
• Experience building LLM-powered applications or AI infrastructure

Responsibilities

• Owns architecture, implementation, and operational health for a platform domain (e.g., agent lifecycle, model orchestration)
• Designs services and interfaces for long-running tasks with durability and recovery
• Establishes reusable patterns for context packaging and data movement across environments
• Builds or evolves model routing and orchestration with metrics on quality, latency, and cost
• Leads technical discovery with product teams to prioritize platform work
• Defines domain testing, tracing, and operational readiness practices; guides incident resolution

Skills

TypeScript

Python

Production services

APIs

Distributed systems

Observability

Cloud deployment

Secure design

LLM features

Mentoring

Education

Bachelor's degree in Computer Science or related field

Job description

SUMMARY

Responsible for leading a substantial technical domain within the AI platform and makes it dependable for use across multiple products. Independently translates ambiguous needs into architecture and staged delivery, then remains accountable for operational performance and adoption. Raises engineering quality through technical guidance, mentorship, and practical standards for shared AI systems.

ESSENTIAL DUTIES AND RESPONSIBILITIES

Owns architecture, implementation, and operational health for a platform domain, such as agent lifecycle and state, sandbox context exchange, model orchestration, or tracing and evaluation.

Designs services and interfaces that support long-running tasks across deployments, restarts, retries, inspection, and replay, with appropriate durability and failure recovery.

Establishes reusable patterns for context packaging and retrieval across execution environments, including clear boundaries for access, retention, and data movement.

Builds or evolves model routing and orchestration capabilities; defines measurements for quality, latency, reliability, and cost per task and uses them to guide improvements.

Leads technical discovery with product teams, identifies common needs, and prioritizes platform work that reduces duplicated engineering and unplanned interruptions.

Defines domain-level testing, tracing, evaluation, and operational readiness practices; reviews production signals and leads resolution of complex incidents in the domain.

Produces architecture documentation and migration guidance that enable engineers to adopt platform capabilities without extensive one-to-one support.

Mentors engineers, reviews designs and code, and contributes to technical planning across the AI engineering group.

Performs all duties in accordance with all company policies and procedures, and all federal, state and local laws, wherein the Company operates.

Performs other duties as assigned.

QUALIFICATIONS

Bachelor's Degreein Computer Science, Software Engineering, a related technical field, or equivalent combination of education and work experience.

Five (5) or more yearsof software engineering experience, or equivalent practical experience, including ownership of production services.

Experience building LLM-powered applications or AI infrastructure.

SKILLS

Advanced proficiency in TypeScript and/or Python and experience designing production services and APIs

Knowledge of distributed systems, asynchronous job processing, persistence, observability, clouddeployment, and secure system design

Experience shipping and supporting LLM features, including practical tradeoffs among model behavior, latency, and cost

Ability to lead technical work from ambiguous problem definition through design, implementation, rollout, and operational improvement

Ability to make platform interfaces reusable and understandable across teams with different product needs

Ability to influence technical decisions, communicate risk and tradeoffs, and mentor engineers across experience levels

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