🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
Senior Associate MLOps / LLMOps Engineer Role: Senior Associate MLOps / LLMOps Engineer Level: Senior Associate Tower: AI Platform Engineering & MLOps (AI Managed Services) Experience: 510 years Key Skills: AWS Cloud & Infrastructure; MLOps & LLMOps; DevOps & CI/CD; Model & Artifact Versioning; Secure Deployments; Observability & Release Governance Educational Qualification: Bachelors degree in Computer Science, Engineering, or related field (Masters or relevant cloud/DevOps certifications preferred) Work Location: Bangalore and Hyderabad (based on your preference) Job Description As a Senior Associate MLOps / LLMOps Engineer, you will design, build, and operate cloud-native AI and ML delivery pipelines that enable reliable, secure, and governed promotion of models and AI services from development to production. You will partner with AI engineers, data scientists, and operations teams to ensure models, prompts, and AI services are versioned, monitored, and deployed with confidence in an enterprise AWS environment. This role is hands-on and execution-focused, emphasizing automation, reliability, and controlled production releases for ML and LLM-based systems. Key Responsibilities AWS Cloud & Infrastructure Engineering Build and maintain AWS-based infrastructure supporting ML, LLM, and AI platforms.Use infrastructure-as-code principles to ensure repeatable and auditable environments.Configure IAM roles, networking, logging, and monitoring aligned to enterprise standards. MLOps & LLMOps Enablement Implement MLOps and LLMOps patterns to support model training, packaging, deployment, and lifecycle management.Support deployment of traditional ML models as well as LLM-based services and workflows.Enable reproducibility across environments through standardized pipelines and artifacts. CI/CD & DevOps Automation Design and maintain GitHub-based CI/CD pipelines for ML models, AI services, and infrastructure changes.Automate build, test, packaging, and deployment workflows.Enforce quality gates and approvals prior to environment promotion. Versioning & Release Management Manage versioning of models, prompts, configurations, and artifacts across environments.Support controlled promotion from development to test, staging, and production.Implement rollback strategies and release validation checks to minimize production risk. Secrets & Configuration Management Securely manage secrets, credentials, and sensitive configuration using AWS-native and approved enterprise tooling.Enforce least-privilege access and rotation policies.Ensure separation of configuration across environments. Deployment & Environment Management Deploy AI and ML services using containerized and cloud-native patterns.Support blue/green, canary, or phased deployments where applicable.Ensure deployments are repeatable, traceable, and compliant with change governance. Monitoring, Logging & Observability Implement monitoring and alerting for AI services, model endpoints, and pipelines.Track service health, deployment status, and runtime performance.Support operational dashboards and metrics for platform and service visibility. Production Support & Controlled Promotion Partner with operations teams to support production readiness and stability.Participate in release readiness reviews and production cutovers.Ensure promotion to production follows defined governance, approvals, and validation criteria. Collaboration & Continuous Improvement Collaborate with AI engineers, data scientists, and platform teams to streamline delivery workflows.Identify opportunities to improve reliability, security, and developer productivity.Contribute reusable pipeline templates, standards, and documentation. Required Skills Hands-on experience with AWS cloud services and infrastructure.Strong understanding of MLOps and LLMOps concepts and lifecycle management.Experience building CI/CD pipelines using GitHub.Solid DevOps fundamentals, including automation and environment management.Experience managing secrets and secure configurations.Familiarity with model and artifact versioning practices.Experience deploying services and supporting controlled production releases.Strong collaboration and documentation skills. Preferred Skills Experience with containerized deployments and orchestration platforms.Familiarity with enterprise monitoring and logging tools.Exposure to governance, risk, and compliance requirements for AI systems.AWS certifications (Developer, DevOps Engineer, Solutions Architect).Experience supporting regulated or large-scale enterprise environments. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.