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
More Information
Application Details
- Organization DetailsTCS / Tata Consultancy Services
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