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AI Engineer
Bridgeway Benefit Technologies. Contribute to the technical direction for AI-enabled systems in partnership with senior engineers and the architecture team.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and integrating AI-enabled systems, with a strong focus on Python development, LLM orchestration, and compliance with industry standards such as HIPAA and SOC 2. Capable of collaborating across teams to enhance AI capabilities and ensure high-quality production outcomes.
Highest-signal resume keywords
Python DevelopmentLLM OrchestrationAI Compliance (HIPAA, SOC 2, NIST)Data Pipeline IntegrationRetrieval-Augmented Systems
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
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Hard Skills
PythonLLM IntegrationData Pipeline DevelopmentProgrammatic LLM OrchestrationEvaluation FrameworksCI/CDInfrastructure-as-CodeMLOpsGenerative AI ToolsMulti-Tenant SaaS Data Isolation
Soft Skills
Strong Written CommunicationStrong Verbal Communication
Tools & Technologies
DatabricksAzureAWSClaudeCopilot
Industry Keywords
AI-Enabled SystemsML-Backed SystemsProduction QualityAudit LoggingData Residency
Tech Stack
Tools & technologiesAWSAzurePython
About the role
Key responsibilities & impact- Contribute to the technical direction for AI-enabled systems 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 applying LLMs and retrieval to internal and product problems.
- Integrate LLM APIs into existing systems using established guidance on model selection, cost profile, and failure handling.
- Ensure AI capabilities honor schema-per-tenant and PHI-boundary patterns within the multi-tenant model.
- Partner with the data and analytics team on retrieval- and LLM-backed features for the lakehouse and analytics layer, including embedding, chunking, retrieval evaluation, semantic search, summarization, anomaly narration, and contextual guidance.
- Contribute to prompt libraries, evaluation frameworks, reference implementations, and reusable components; help refine standards for model selection, evaluation, testing, observability, and cost management.
- Evaluate emerging AI tools and provide input on build-versus-buy decisions.
- Build AI implementations compliant with HIPAA, SOC 2, and NIST, including PHI handling, data residency, audit logging, BAA coverage, prompt-injection defenses, LLM DLP, model access governance, and secrets handling.
- Maintain documentation of AI systems, data flows, and risk assessments for audit and compliance review.
- Collaborate with engineering, product, customer success, security, data, and operations to identify automation opportunities, translate them into technical plans, and contribute to the customer-facing AI roadmap.
- Participate in operational quality and on-call rotation, including incident response, post-incident learning, observability, and feedback loops to improve retrieval quality, prompt behavior, and agent reliability.
- Track production AI cost, quality, and adoption metrics and report outcomes to the manager, architecture team, and team leads.
- Apply enterprise AI-integration best practices and share knowledge with peers.
Requirements
What you’ll need- 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.
- Experience using generative AI tools such as Claude and Copilot to improve productivity and deliver high-quality outcomes.
- Production experience in a regulated industry, with practical familiarity with HIPAA, SOC 2, or NIST, and with multi-tenant SaaS data isolation and identity/access patterns for B2B platforms (preferred).
- Bachelor's or master's degree in Computer Science, Engineering, Data Science, or related field.
Benefits
Comp & perks- Remote position
- Equal Opportunity Employer