Applied AI Engineer

mpathic

. Own, design, build, and run the LLM Gateway and the single path through which production services call model providers .

Posted 11/10/2026full-timeRemote • United StatesMid-LevelSenior💰 $150,000 - $175,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in backend software engineering with a focus on multi-tenant SaaS services and APIs, particularly in the context of LLMs. Proficient in implementing observability, security, and compliance measures while collaborating across teams to deliver scalable production services.

Highest-signal resume keywords

Backend Software EngineeringProduction Experience with LLM APIsStrong PythonAWS and Infrastructure as CodeObservability Tooling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Backend Software EngineeringProduction Experience with LLM APIsStrong PythonEvaluation Pipeline DesignVersion ControlFeature FlagsApplication SecurityCI/CD IntegrationPrompt ManagementTool Use

Soft Skills

CollaborationIncident Response Leadership

Tools & Technologies

AWSOpenTelemetryDatadogPulumiTerraformCDKLinearContainers (ECS or Similar)

Certifications & Qualifications

HIPAASOC 2ISO 27001FDA 21 CFR Part 11

Industry Keywords

Multi-Tenant SaaSHealthcareLife SciencesRegulated DomainsSensitive Data Handling

Tech Stack

Tools & technologies

AWSPythonTerraform

About the role

Key responsibilities & impact

Own, design, build, and run the LLM Gateway and the single path through which production services call model providers
Build logging, cost attribution, rate limiting, failover, and protected health information (PHI) controls
Remove direct provider API keys from services so model access occurs through the gateway
Maintain the approved catalog of models, providers, endpoints, and model features
Document data retention, BAA coverage, and version pinning, and record additions in decision records
Version prompts and model configurations alongside code
Run AI/ML evaluations as CI release gates with staged rollout and rollback
Provide self-service templates for sandbox and staging while gating production
Turn validated AI/ML prototypes, evaluation harnesses, and acceptance thresholds into scalable, observable production services
Advise on model cost, latency, and availability
Instrument LLM services with OpenTelemetry for latency, failures, token usage, cost, and output quality
Monitor evaluation drift, join on-call, and lead incident response for LLM-backed services
Produce audit evidence with Security and Compliance for SOC 2, ISO 27001, HIPAA, and FDA 21 CFR Part 11
Collaborate with the CTO, platform engineering, ML, research, domain experts, sales, and delivery teams

Requirements

What you’ll need

5+ years of backend software engineering, including multi-tenant SaaS services and APIs in production
Production experience building on commercial LLM APIs, including prompt management, structured outputs, and tool use
Hands-on experience designing evaluation pipelines for LLM output and wiring them into CI/CD as release gates
Strong Python
AWS, containers (ECS or similar), and infrastructure as code (Pulumi, Terraform, or CDK)
Observability tooling such as OpenTelemetry, Datadog, or similar
Experience with version control, feature flags, CI, and an issue tracker such as Linear
Working knowledge of application security, including secrets management, access control, and handling sensitive data
Preferred: experience in regulated domains such as healthcare or life sciences, including HIPAA, SOC 2, ISO 27001, or FDA 21 CFR Part 11 validated systems
Preferred: experience building an internal developer platform or paved road used by other teams
Preferred: experience with LLM-as-judge evaluation, red teaming, or conversational analytics
Preferred: experience with training or fine-tuning models from scratch