AI Engineer

Bridgeway is seeking an AI Engineer to help build and operate large language model and intelligent automation capabilities across our products and internal tooling. The AI Engineer will implement production systems against the patterns and reference implementations set by our architecture team, contribute to design discussions, and partner with senior engineers to ship reliable AI-enabled features. This is a hands-on engineering role focused primarily on building. We are seeking a technically curious individual who brings solid Python experience and a pragmatic approach to AI-enabled capabilities. A successful AI Engineer is a strong builder with a growing background in production software development and the kind of engineering judgment that turns architectural patterns into reliable working systems.

This is a remote position with preference given to East Coast candidates.

Responsibilities:

Contribute to the technical direction for AI-enabled systems at Bridgeway, in partnership with senior engineers and the architecture team.

Participate in design reviews for AI-adjacent features and incorporate feedback from senior engineers and architects.

Build production-quality Python services, pipelines, and integrations that apply LLMs and retrieval to concrete internal and product problems.

Integrate LLM APIs into existing systems, following established guidance on model selection, cost profile, and failure-handling for each use case.

Ensure AI capabilities correctly honor schema-per-tenant and PHI-boundary patterns within our multi-tenant model, partnering with the data team that owns those patterns.

Partner with the data and analytics team to integrate retrieval- and LLM-backed features into the lakehouse and analytics layer — embedding and chunking choices, retrieval evaluation, semantic search, summarization, anomaly narration, and contextual guidance for fund office users — with the data team owning pipeline build and operation.

Contribute to prompt libraries, evaluation frameworks, reference implementations, and reusable components that help engineers ship AI-touching features consistently and safely; follow and help refine development standards for model selection, evaluation, testing, observability, and cost management.

Help evaluate emerging AI tools and provide input on build-vs-buy decisions anchored in Bridgeway's technical, cost, and compliance context.

Build AI implementations that comply with HIPAA, SOC 2, and NIST — with particular attention to PHI handling, data residency, audit logging, and BAA coverage — and work with information security to apply AI-specific controls including prompt-injection defenses, DLP for LLM interactions, model access governance, and secrets handling.

Maintain documentation of AI systems, data flows, and risk assessments sufficient for audit and compliance review.

Collaborate with engineering, product, customer success, security, data, and operations to identify automation opportunities, help translate them into technical plans, and contribute to the roadmap for future customer-facing AI capabilities.

Participate in the operational quality and on-call rotation for AI systems — including incident response, post-incident learning, and instrumented observability — and help build feedback loops from users to continuously refine retrieval quality, prompt behavior, and agent reliability.

Track cost, quality, and adoption metrics for AI systems in production and report outcomes to your manager, the architecture team, and the team leads.

Apply best practices in enterprise AI-integration and share knowledge with peers.

Qualifications:

3–5 years of software engineering experience, with at least one year working on AI- or ML-backed systems, including contributions to at least one LLM-backed system in production.

Working knowledge of programmatic LLM orchestration, evaluation frameworks, and agent and tool-using systems, including exposure to securely connecting enterprise APIs to agentic systems.

Strong Python skills for production services, data pipelines, and integration work.

Experience building retrieval-augmented systems including familiarity with enterprise data architectures such as Databricks and Azure/AWS.

Strong written and verbal communication.

Familiarity with CI/CD, infrastructure-as-code, and MLOps practices for AI/ML systems.