Devsecops And Sre

Job Description

United States, North Carolina, Charlotte

Oct 29, 2026

Must Have Technical/Functional Skills:

• 8+ years of software engineering experience, including 3+ years in senior/staff roles and 2+ years
• with AI/ML experience
• Deep expertise in Python and TypeScript - able to architect and build across the full stack
• Expert-level AWS - ECS, Lambda, DynamoDB, IAM, VPC, CloudFormation/Terraform, multi-account strategies
• Production experience - incident management, SLOs, error budgets, capacity planning
• CI/CD platform ownership at scale - GitLab CI, pipeline-as-code, environment orchestration
• Security engineering background - threat modeling, IAM design, vulnerability management, compliance (FRB, SOX/FFIEC)
• Experience with AI/ML systems - LLM integration, prompt engineering, RAG architectures, embedding models
• Track record of mentoring engineers and raising team capabilities
• Excellent communication - can translate technical decisions for leadership and collaborate across teams
• Experience in financial services or similarly regulated environment preferred Must Have (Elevate-specific),
• Demonstrated ability to operate independently with minimal direction
• History of driving organizational-level technical improvements
• Comfort with ambiguity - define the problem, not just solve it
• Player-coach mindset - hands-on coding while mentoring and leading
• Ability to context-switch between strategic (architecture, roadmap) and tactical (debugging, shipping)
• EventBridge, IAM, VPC) | Terraform | GitLab CI | Docker | Dynatrace (DQL) | Jira/Confluence/ServiceNow APIs | AI Gateway (GPT/Claude/open-source LLMs)

Technical Leadership & Application Architecture (30%)

Drive decisions & resolve challenges for internal AI platform - service design, data modeling, API contracts, scaling strategy, intelligence build - Establish and enforce engineering standards - code quality, testing practices, security controls, deployment patterns - Lead design reviews and technical decision-making for the team - Define and evolve the team's AI governance framework - trust levels, approval chains, model evaluation criteria - Represent engineering in cross-functional forums - architecture review boards, security assessments, compliance audits - Own technical roadmap alignment between DevSecOps, RM, and Cognitive Engineering workstreams

Production Engineering & SRE (25%)

Own production reliability - SLO definition, error budgets, availability targets - Lead complex incident response - triage, cross-team coordination, executive communication - Design and implement self-healing automation - detection, decision, approval, remediation pipelines - Architect observability strategy - Dynatrace/internal logging instrumentations, alerting dashboards, DQL queries - Manage on-call rotation; mentor team on debugging production issues - Drive toil reduction - identify repetitive operational work, automate or eliminate

DevSecOps & Security (20%)

Own CI/CD platform architecture - pipeline optimization, caching, parallel execution, security gates - Design and implement security controls - IAM policies, network segmentation, secrets management, vulnerability remediation - Lead infrastructure-as-code practices - Terraform module design, drift detection, compliance validation - Manage AWS infrastructure at scale - ECS clusters, , DynamoDB capacity planning, cost optimization - Drive container security - image hardening, ECR scanning, runtime protection

AI & Cognitive Engineering (25%)

Lead internal AI platform technical direction - self-heal engine, knowledge token budgets, model evaluation - Build the team's AI engineering capabilities - Related evolving tools enablement, Guild Commons governance - Own the Ally AI Gateway integration - model selection, fallback strategies, cost optimization - Pioneer AGI-readiness patterns - multi-model orchestration, approval chains, confidence-gated autonomy - Drive adoption strategy - enable other teams to contribute personas and