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About This Role
Job Description: We are seeking a highly capable Senior ML Engineer / MLOps Engineer with strong experience in building, deploying, and scaling machine learning systems in production. The ideal candidate will have hands\-on expertise across the end\-to\-end ML lifecycle , including data pipelines, model development, deployment, and monitoring, along with a strong foundation in cloud\-native architectures. This role requires close collaboration with data scientists and stakeholders to operationalize ML models for business\-critical use cases such as personalization, recommendations, and NLP , while ensuring scalability, reliability, and performance in production environments.
Responsibilities: Design, develop, and deploy end\-to\-end ML pipelines covering data ingestion, transformation, feature engineering, model training, evaluation, and production deployment. Deploy and scale ML models on cloud platforms such as AWS (SageMaker, EKS, Lambda) or GCP (Vertex AI, GKE, Cloud Functions) , ensuring robust and cost\-efficient architectures. Build and maintain CI/CD/CT pipelines using tools like GitHub Actions, Jenkins, or cloud\-native services to automate model training, testing, and deployment. Containerize applications using Docker and orchestrate using Kubernetes , while managing infrastructure through Terraform or CloudFormation .
Implement model lifecycle management practices , including model registries, versioning, and feature stores (e.g., MLflow, Feast), and establish strong observability frameworks using Prometheus and Grafana. Develop monitoring systems to track ML performance metrics, data drift, model drift, and overall model health , ensuring timely retraining and optimization. Build scalable data pipelines using Airflow, Spark, and SQL , and work with orchestration tools such as Apache Airflow or AWS Step Functions . Collaborate closely with data scientists to productionize ML models for real\-time and batch inference, enable A/B testing where applicable, and ensure smooth delivery of client\-facing solutions. Provide mentorship to junior engineers and drive adoption of best practices in MLOps and software engineering.
Qualifications: Strong experience in ML Engineering / MLOps with demonstrated delivery of end\-to\-end ML solutions in production environments. Proficiency in Python and advanced SQL , along with hands\-on experience in ML frameworks such as Scikit\-learn, TensorFlow, and PyTorch . Solid understanding of machine learning algorithms, evaluation techniques, performance metrics, and validation strategies . Hands\-on expertise in cloud platforms (AWS or GCP) , containerization (Docker), orchestration (Kubernetes), and CI/CD tools (Jenkins, GitHub Actions). Familiarity with MLflow, Feast, Prometheus, Grafana , and modern model monitoring practices including data and model drift detection .
Strong problem\-solving, communication, and stakeholder management skills with the ability to work independently in fast\-paced environments. Bachelor’s degree in computer science, Engineering, or a related field preferred.
Nice\-to\-have: Experience with real\-time ML serving frameworks (KFServing, Seldon, Ray Serve) , A/B testing, and experimentation platforms. Exposure to media, subscription, or recommender systems , along with knowledge of experiment design and causal inference , will be an added advantage.
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 EXL Service, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
EXL Service AI Hiring
EXL Service has 9 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer, AI Architect. Positions span US, Jersey City, NJ, US. Compensation range: $150K - $280K.
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.
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