Back
Tier-1 brand, remote India scope, metro preference and mid-level experience drive high applicant competition.
Specialized LLMops, enterprise integration, security and regulated-industry experience limit cross-industry transferability.
Explicit 6–9 years plus mandatory LLMops, AWS, Kubernetes, Terraform, security and CI/CD requirements make filtering strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and manage automation frameworks and CI/CD pipelines specifically for AI solution deployments on AWS.
Lead integration and deployment of LLM and AI solutions ensuring security, governance, scalability, and observability in production.
Mentor engineering team members and own deployment architecture and standards across engagements within forward-deployed, client-embedded teams.
6-9 years work experience in DevOps or platform engineering roles with production deployment ownership.
Strong expertise in CI/CD tools (GitHub Actions, Jenkins, ArgoCD), Docker, Kubernetes, Terraform and AWS services including Bedrock, SageMaker, Lambda, ECS, EKS.
Deep knowledge of identity, security, networking, and enterprise integrations relevant to regulated and legacy environments.
Hands-on experience with LLMOps deployments including model serving, scaling, retrieval infrastructure, observability, and responsible AI controls.
Experienced in forward-deployed consulting environments working embedded with enterprise customers, handling complex AI deployment challenges.
Demonstrates operational ownership, clear stakeholder communication, and ability to lead and mentor engineering teams.
Skilled in building scalable, secure AI deployment architectures with strong focus on automation, cost management, and compliance with responsible AI principles.