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About This Role
AWS MLOps Enginner
We are looking for an ML Engineer / MLOps Engineer with experience in ML lifecycle management, model deployment, and cloud\-based ML systems. The ideal candidate should have hands\-on expertise in training, evaluating, deploying, versioning, and monitoring ML models in production environments.
Required Skills
ML / MLOps
2\+ years of experience in:
- MLflow / Spark ML
- Python and ML libraries
- Model training and evaluation
- Model registration, versioning, and lifecycle management
- Model deployment, CI/CD, and GitHub Actions
- Model monitoring and performance metrics
AWS Cloud
2\+ years of experience with:
- Amazon ECS
- Amazon ECR
- Amazon API Gateway
- Amazon RDS
- Application Load Balancer
- Amazon S3
Backend
1\+ year of experience in:
- FastAPI
- REST API development
- SQL and relational data modeling
- PostgreSQL
- SQLAlchemy / ORM
Good to Have
Preferred Skills
Experience with:
- Agentic AI
- Databricks (Unity Catalog, Jobs \& Workflows, Access Management)
Someone who has worked on end\-to\-end ML lifecycle management including:
- training
- deployment
- versioning
- monitoring
- production support
The base salary for this position is $92,250 \- $120,000 plus incentives that align with individual and company performance. Actual salaries will vary based on work location, qualifications, skills, education, experience, and competencies. Benefits available to eligible employees in this role include medical, dental, and vision insurance, comprehensive employee assistance program, 401(k) retirement plan, paid time off and holidays and paid learning days.
The deadline to apply for this position is: 06/17/2026\. This position is for an existing, immediate vacancy. We are currently seeking to fill this role with an individual who can start as soon as possible.
As part of the hiring process, candidates may be required to undergo background screening and identity verification, where permitted by applicable law and consistent with the requirements of the role. Certain verification processes used by the Company or its service providers may involve technologies that rely on biometric identifiers or biometric information, where permitted by law. If biometric identifiers or biometric information are collected, used, or stored, the Company will provide the legally required disclosures and obtain any required written consent prior to such collection, and will handle such information in accordance with applicable biometric privacy laws and Company policies.
Physical and Mental Requirements
The employee is regularly required to operate a computer, keyboard, telephone/headset, and/or other office equipment as essential functions of this position. Work is generally sedentary in nature.
Equal Employment Opportunity
Concentrix is an equal opportunity and affirmative action (EEO\-AA) employer. We promote equal opportunity to all qualified individuals and do not discriminate in any phase of the employment process based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, pregnancy or related condition, disability, status as a protected veteran, or any other basis protected by law.
For more information regarding your EEO rights as an applicant, please visit the following websites:
- English: https://www.eeoc.gov/sites/default/files/2023\-06/22\-088\_EEOC\_KnowYourRights6\.12\.pdf
- Spanish: https://www.eeoc.gov/sites/default/files/2023\-06/22\-088\_EEOC\_KnowYourRightsSp6\.12\.pdf
Accommodation
Concentrix welcomes and encourages applications from candidates with disabilities and is committed to providing an inclusive recruitment process. If you require reasonable accommodation to participate in any stage of the application or interview process, please let us know. Requests may be made by contacting [email protected]. All information will be treated confidentially and used solely to facilitate your participation in the recruitment process.
Artificial Intelligence
As part of our recruitment process, we may use artificial intelligence (AI) tools to assist in the screening and/or assessment of job applicants. These tools could be used to evaluate resumes, applications, and other materials submitted to help us identify the best candidates for the role.
Work Authorization
In accordance with federal law, only applicants who are legally authorized to work in the United States will be considered for this position. Must reside in the United States or have a valid U.S. address for residence.
For further information on available work states and Equal Employment Opportunity as an applicant, please visit:https://jobs.concentrix.com/north\-america\-equal\-employment\-opportunity\-information/
\#WFH
\#LI\-Remote
Physical and Mental Requirements:
The employee is regularly required to operate a computer, keyboard, telephone/headset, and/or other office equipment as essential functions of this position. Work is generally sedentary in nature.
Equal Employment Opportunity:
Concentrix is an equal opportunity and affirmative action (EEO\-AA) employer. We promote equal opportunity to all qualified individuals and do not discriminate in any phase of the employment process based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, pregnancy or related condition, disability, status as a protected veteran, or any other basis protected by law.
For more information regarding your EEO rights as an applicant, please visit the following websites:
- English
- Spanish
Accommodation:
Concentrix welcomes and encourages applications from candidates with disabilities and is committed to providing an inclusive recruitment process. If you require reasonable accommodation to participate in any stage of the application or interview process, please let us know. Requests may be made by contacting [email protected]. All information will be treated confidentially and used solely to facilitate your participation in the recruitment process.
Artificial Intelligence:
As part of our recruitment process, we may use artificial intelligence (AI) tools to assist in the screening and/or assessment of job applicants. These tools could be used to evaluate resumes, applications, and other materials submitted to help us identify the best candidates for the role.
Work Authorization:
In accordance with federal law, only applicants who are legally authorized to work in the United States will be considered for this position. Must reside in the United States or have a valid U.S. address for residence.
For further information on available work states and Equal Employment Opportunity as an applicant, please click HERE
Salary Context
This $92K-$120K range is in the lower quartile for MLOps Engineer roles in our dataset (median: $190K across 22 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 3,823 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Concentrix, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $217,200 based on 87 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($106K) sits 51% below the category median. Disclosed range: $92K to $120K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Concentrix AI Hiring
Concentrix has 4 open AI roles right now. They're hiring across MLOps Engineer, AI/ML Engineer. Positions span Austin, TX, US, AL, US, UT, US. Compensation range: $120K - $220K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national median.
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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