Job description

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) • Tech Stack, Python 3.12 | FastAPI | React 19 | TypeScript | AWS (ECS, Lambda, DynamoDB, • EventBridge, IAM, VPC) | Terraform | GitLab CI | Docker | Dynatrace (DQL) | Jira/Confluence/ServiceNow APIs | AI Gateway (GPT/Claude/open-source LLMs) Roles & Responsibilities: 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 t argets - 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 execut ion, 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, Lambda@Edge, 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 core, proactive monitoring, COG grid - Design AI pipeline architecture — LLM routing, prompt management, token budgets, model evaluation - Build the team’s AI engineering capabilities — Realted 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 extend the platform Salary: $68,000 - $110,000 </div

Requirements

Must-haves

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
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)
Tech Stack, Python 3.12 | FastAPI | React 19 | TypeScript | AWS (ECS, Lambda, DynamoDB,
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

Nice-to-haves

Experience in financial services or similarly regulated environment preferred Must Have (Elevate-specific),

Application prep

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Keywords to mirror

aimlpythontypescriptawsci/cdllmreactdockerjiraapi

Platform: careers-page
Effort: medium