AI Enablement Engineer

Payliance

AI Enablement Engineer developing AI platforms at Payliance. Focused on building reliable, governed AI systems across the organization.

Posted 7/28/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in AI inference platform operations, particularly with Amazon Bedrock, and possesses strong software engineering skills in C#/.NET and Python. Capable of designing reliable AI services with a focus on observability, security, and cost-effective performance.

Highest-signal resume keywords

Amazon BedrockC#/.NET DevelopmentPython DevelopmentReliability EngineeringAWS Fluency

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Large Language ModelsPrompt EngineeringObservabilityCI/CD AutomationData Access Scoping

Soft Skills

Exceptional Communication SkillsTeaching AI Concepts

Tools & Technologies

AWS LambdaECS FargateCloudFormationGitHub Actions

Industry Keywords

AI GovernancePCI ComplianceIdentity and Access ManagementSecurity-first Mindset

Tech Stack

Tools & technologies

AWS.NETPython

About the role

Key responsibilities & impact

Operate and evolve Payliance's AI inference platform on Amazon Bedrock, including model selection, routing logic, and version pinning across the Claude model family.
Build and maintain internal AI services and integration layers (C#/.NET, Python) that connect Claude to enterprise systems and workflows.
Design cost-aware inference strategies — prompt caching, model tiering, and intent-based routing — that balance capability against spend.
Own the reliability of AI services with the same rigor applied to the payment platform: define SLOs, build observability (logging, tracing, alerting), plan capacity, and design for graceful degradation when models or upstream services falter.
Establish disciplined change management for AI infrastructure — pinned model versions, staged rollouts, and regression testing — so behavior never drifts silently in production.
Design, build, and maintain Claude agents, Skills, and MCP (Model Context Protocol) integrations that connect AI to internal data sources and tools.
Develop reusable agent patterns — retrieval, tool use, structured output, multi-step workflows — that other teams can adopt without starting from scratch.
Author and curate high-quality prompts, skill definitions, and agent instructions, with versioning and review discipline.
Evaluate agent quality systematically: define eval criteria, test for regressions, and validate behavior before promotion to production.
Operate and extend the internal AI skill/agent marketplace: submission pipelines, staging and curation workflows, and publication gates.
Enable non-technical employees — business analysts and beyond — to create and submit agents and Skills through low-friction workflows that don't require engineering tooling or source-control accounts.
Serve as a formal review gate for submitted agents and Skills: assess security posture, data access, prompt quality, and fitness for purpose before publication.
Manage distribution across surfaces — Claude Enterprise, Claude Code, and internal applications — with consistent configuration and rollout controls.
Design and enforce identity-aware access to AI capabilities: SSO/SCIM provisioning, group-based entitlements, and per-user permission propagation to downstream data sources.
Ensure AI tools respect existing data permissions — users should never see data through an agent that they couldn't access directly.
Author and review least-privilege IAM policies for AI infrastructure; avoid broad credential grants in favor of scoped, auditable access patterns.
Establish and maintain AI governance controls appropriate to a PCI-regulated payments environment: data handling policies, audit trails, and model usage boundaries.
Build and maintain AI usage analytics and reporting: adoption metrics, token consumption, and cost breakdowns by team and use case.
Deliver operational visibility to executive stakeholders through automated reporting and dashboards.
Monitor for misuse, anomalous usage patterns, and quality degradation across deployed agents and Skills.
Continuously optimize the cost/performance profile of AI workloads as models, pricing, and usage patterns evolve.
Partner with business teams to identify high-value AI use cases and translate them into working agents, Skills, and workflows.
Train and coach non-technical builders on effective prompt design, agent construction, and responsible AI use.
Author reference architectures, design decisions, and integration patterns that other engineering teams adopt — contributing to Architectural Services' broader practice.
Collaborate with platform engineering and security teams on architecture decisions, integration patterns, and compliance requirements.
Champion pragmatic AI adoption: cut through hype, set realistic expectations, and demonstrate measurable value.

Requirements

What you’ll need

5+ years in software engineering, platform engineering, or DevOps, with 1+ years of hands-on experience building with large language models in production.
Practical LLM platform depth: Amazon Bedrock (or equivalent), model APIs, prompt engineering, structured outputs, and agentic/tool-use patterns.
Software engineering ability in C#/.NET or Python — can design, build, and debug production services, not just scripts.
Reliability engineering discipline: experience defining SLOs, building observability, designing for failure and graceful degradation, and operating services that other teams depend on.
AWS fluency: compute (Lambda, ECS Fargate), IAM, networking fundamentals, and infrastructure-as-code (CloudFormation or CDK).
Identity and access architecture experience: SSO (Entra ID or similar), SCIM provisioning, OAuth flows, and least-privilege permission design.
Security-first mindset with practical experience scoping data access and building auditable, governed systems.
CI/CD and workflow automation experience (GitHub Actions or similar) for building submission, review, and publication pipelines.
Exceptional communication skills — able to teach AI concepts to non-technical audiences and translate business needs into technical designs.

Benefits

Comp & perks

Competitive Base Salary based on experience.
Performance-based annual bonus.
Medical, Dental, and Vision insurance.
401(k) with company match.
Generous PTO plus paid company holidays.
Company-paid life and long-term disability insurance.
Paid parental leave.