DevSecOps and SRE

Tata Consultancy Services · Charlotte, NC · 2 mo ago

Engineering$68k–$110k/yrFull-time

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
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)

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

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

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, Lambda@Edge, DynamoDB capacity planning, cost optimization
Drive container security — image hardening, ECR scanning, runtime protection

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

Bachelor of Computer Science

$68,000 - $110,000

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