MLOps / LLMOps Engineer - Career Guide¶
The role that keeps AI systems shippable and stable: deployment, observability, automation, rollback, and the operational discipline that production AI depends on.
Role Overview¶
| Field | Details |
|---|---|
| Stack Layer | Layer 3 (Inference & Serving) |
| What You Do | Operate the platforms and delivery pipelines that make ML and LLM systems deployable, observable, scalable, and recoverable in production. |
| Also Called | LLMOps Engineer, ML Platform Engineer |
| Salary (US) | Mid: $140-200K / Senior: $180-280K+ |
| Salary (India) | Mid: Rs 15-30 LPA / Senior: Rs 30-55+ LPA |
| Job Availability | Very High |
| Entry Requirements | Bachelor's in CS with strong DevOps, backend, or SRE foundations plus ML or LLM platform experience |
| Last Researched | 2026-03 |
A Day in the Life¶
- 9:00 — Incident review: the model serving endpoint had a 5-minute outage at 3am due to a GPU memory leak
- 9:30 — Fix the root cause: the vLLM container wasn't configured with memory limits, add them to the helm chart
- 10:30 — Update the CI/CD pipeline: add an automated eval gate that blocks deployment if accuracy drops >2%
- 12:00 — Review infrastructure costs: GPU spend is 30% over budget, propose spot instance strategy
- 14:00 — Help the ML team debug a training pipeline failure: the data versioning snapshot was corrupted
- 15:30 — Write runbook for the new model rollback procedure and test it in staging
- 17:00 — Set up alerting for the new LLM endpoint: token throughput, error rate, latency percentiles, and cost per query
Learning Path (from this repo)¶
Phase 1: Prerequisites & Foundation¶
Complete Part 1 of the Learning Path first.
Phase 2: Core Knowledge¶
| # | Topic | Note | Priority | Est. Time |
|---|---|---|---|---|
| 1 | LLMOps | llmops | Must | 3h |
| 2 | Docker and Kubernetes | docker-and-kubernetes | Must | 3h |
| 3 | Model serving | model-serving | Must | 3h |
| 4 | Monitoring and observability | monitoring-observability | Must | 3h |
| 5 | CI/CD for ML | cicd-for-ml | Must | 3h |
| 6 | Cost optimization | cost-optimization | Must | 3h |
| 7 | Cloud ML services | cloud-ml-services | Must | 3h |
| 8 | ML experiment tracking | ml-experiment-tracking | Must | 2h |
| 9 | Data versioning | data-versioning-for-ml | Must | 2h |
Phase 3: Advanced / Differentiating Knowledge¶
| # | Topic | Note | Priority | Est. Time |
|---|---|---|---|---|
| 1 | Latency and throughput engineering | latency-and-throughput-engineering | Good | 3h |
| 2 | Distributed systems fundamentals for AI | distributed-systems-for-ai | Good | 3h |
| 3 | Distributed inference architecture | distributed-inference-and-serving-architecture | Good | 3h |
| 4 | System design for AI interviews | system-design-for-ai-interviews | Good | 2h |
| 5 | AI regulation for builders | ai-regulation | Good | 2h |
Phase 4: External Skills¶
| # | Skill | Recommended Resource | Priority |
|---|---|---|---|
| 1 | Terraform or IaC | official docs and platform projects | Must |
| 2 | Cloud networking and IAM | AWS, GCP, or Azure platform depth | Must |
| 3 | Incident response and runbook discipline | SRE-style ops practice | Must |
Skills Breakdown¶
Must-Have Technical Skills¶
- Deployment automation, rollback, and release discipline
- Observability, alerting, and incident response
- Containers, cloud platforms, and platform operations
Nice-to-Have Technical Skills¶
- GPU and serving performance tuning
- Security and governance awareness
- Multi-model routing and cost engineering
Soft Skills¶
- Operational calm under pressure
- Strong documentation and runbook habits
- Clear collaboration with app, data, and security teams
Resume Bullet Templates¶
Entry Level¶
- Built containerized model serving pipeline with automated deployment, reducing model release time from 3 days to 2 hours
- Implemented monitoring dashboard tracking 15 metrics (latency, throughput, error rate, cost) across 3 production LLM endpoints
Mid Level¶
- Designed evaluation-gated CI/CD pipeline for LLM deployments, catching 8 quality regressions before production in the first quarter
- Led GPU infrastructure optimization reducing monthly compute costs by 40% through spot instances and dynamic scaling
Senior Level¶
- Architected ML platform serving 25 production models with zero-downtime deployments, automated rollback, and 99.95% uptime SLA
- Established LLMOps best practices adopted across 4 engineering teams: standardized eval gates, cost tracking, incident response playbooks
Portfolio Project Ideas¶
| Project | Description | Skills Demonstrated | Difficulty |
|---|---|---|---|
| LLMOps platform skeleton | Deploy model service with tracing, alerts, CI/CD, and rollback plan | MLOps, serving, observability | Medium |
| Evaluation-gated release pipeline | Automated release flow with offline evals and canary checks | CI/CD, governance, quality gates | Medium |
| Cost optimization dashboard | Real-time GPU and API cost tracking with usage attribution per team/model | Cost engineering, monitoring, dashboarding | Medium |
| Incident response simulator | Automated chaos testing for ML services with playbook validation | Reliability, incident response, automation | Hard |
Take-Home Project Examples¶
Example 1: Deploy and Monitor an LLM Service¶
Brief: Deploy a provided LLM model using Docker, set up basic monitoring (latency, error rate, cost), and implement a rollback mechanism.
Evaluation criteria: Deployment automation quality, monitoring coverage, rollback reliability, documentation.
Time: 4-6 hours
Example 2: CI/CD Pipeline with Eval Gate¶
Brief: Build a GitHub Actions pipeline that runs an eval suite before deploying a model update. Block deployment if accuracy drops below a threshold.
Evaluation criteria: Pipeline design, eval gate reliability, failure handling, scalability of approach.
Time: 3-4 hours
Interview Preparation¶
Review llmops, docker-and-kubernetes, monitoring-observability, and cicd-for-ml.
Common questions:
- What makes LLMOps different from normal DevOps?
- How do you ship a model or prompt change safely?
- What metrics matter most in production AI operations?
System Design Interview Scenarios¶
Scenario 1: Design an LLM deployment and rollback system - Requirements: Deploy model updates weekly, rollback within 5 minutes, zero downtime, eval gates - Key decisions: Blue-green vs canary deployment, eval automation, state management, monitoring - Scoring: Reliability approach, speed of rollback, automation coverage, incident response
Scenario 2: Design a multi-model serving platform - Requirements: Serve 10 models (classical ML + LLMs), optimize GPU utilization, per-team cost attribution - Key decisions: Orchestration (K8s), GPU sharing, autoscaling, cost tracking, alerting - Scoring: Resource efficiency, cost modeling, scalability, operational complexity
30-60-90 Day Onboarding Plan¶
| Phase | Focus | Key Deliverables |
|---|---|---|
| Days 1-30 (Learn) | Understand the deployment pipeline, monitoring stack, and operational runbooks | Shadow 3 on-call rotations, deploy a model to staging, review all existing runbooks |
| Days 31-60 (Contribute) | Improve one operational workflow | Add monitoring coverage for a gap area, automate a manual deployment step, write a new runbook |
| Days 61-90 (Own) | Take ownership of a production ML platform component | Own the CI/CD pipeline, establish SLOs for a service, drive a cost optimization initiative |
Career Progression¶
| Direction | Roles |
|---|---|
| Entry points | DevOps engineer, backend engineer, ML engineer |
| Next level | Senior MLOps, ML Platform Lead, AI Infrastructure Engineer |
| Lateral moves | ML Engineer, AI Infrastructure Engineer, Platform Architect |
Companies Hiring This Role¶
| Tier | Companies |
|---|---|
| Tier 1 | cloud providers, major SaaS companies, AI startups |
| Broad market | any enterprise shipping AI at scale, platform-first data orgs |
Sources¶
- GenAI Career Roles - Complete Reference (2026)
- Repo notes linked above