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About This Role
Citrin Cooperman offers a dynamic work environment, fostering professional growth and collaboration. We’re continuously seeking talented individuals who bring a problem\-solving mindset, fresh perspectives, and sharp technical expertise. We know you have choices, so our team of collaborative, innovative professionals are ready to support your professional development. At Citrin Cooperman, we offer competitive compensation and benefits and most importantly, the flexibility to manage your personal and professional life to focus on what matters most to you!
We are seeking a Senior – MLOps/LLMOps Engineer, Development, to join our Development team within the Information Technology department. The AI Solutions team is the vanguard of our enterprise AI competency, bridging the gap between rapid generative AI pilots and our enterprise operations. As we industrialize these advanced applications, you’ll build the operational backbone for our non\-deterministic systems.
In this critical deployment and observability role, you’ll define how generative AI and agentic workflows are shipped to production. Working with frontier models (Anthropic, Google, OpenAI) and custom frameworks (LangGraph), you’ll transition pilot code into robust solutions, automated with CI/CD pipelines. You’ll own the infrastructure for prompt versioning, while establishing automated evaluation gates (e.g., LLM\-as\-a\-judge), and implementing the deep telemetry required to monitor token costs, latency, and hallucination rates. The ideal candidate has a strong DevOps foundation but has successfully pivoted into the unique challenges of machine learning and generative AI operations, as well as views observability as the ultimate defense against model drift.
Responsibilities are, but not limited to
- LLMOps CI/CD Pipelines: Design and build automated deployment pipelines specifically for generative AI applications. Ensure that updates to prompts, LangGraph state machines, or RAG retrieval logic can be safely promoted across environments (Dev, Test, Prod).
- Evaluation Infrastructure: Deploy and manage the infrastructure required for continuous AI evaluation (e.g., LangSmith, Braintrust, or custom evaluation harnesses). Embed precision, recall, and toxicity checks directly into the deployment gates.
- Telemetry \& Observability: Instrument the AI applications to capture deep operational metrics. Build dashboards to monitor token consumption, end\-to\-end latency, reasoning traces, and API failure rates across multiple LLM providers.
- Prompt \& Model Registry Management: Implement version control for prompts and model configurations, ensuring the enterprise has a strict, auditable history of what instructions are running in production at any given time.
- Guardrails \& Content Filtering: Integrate input/output guardrails (e.g., Azure AI Content Safety, NeMo Guardrails) into the application flow to automatically block prompt injection attacks, PII leakage, or off\-topic responses.
- Cost Management (FinOps for AI): Actively monitor the financial footprint of our AI solutions. Set up alerting for token usage spikes and work with AI Engineers to optimize embedding and retrieval strategies for cost efficiency.
The ideal candidate must:
- Have a bachelor’s degree in computer science, information technology, engineering, or equivalent practical experience.
- Be Databricks Certified: Machine Learning Professional
- Be Microsoft Certified: Azure DevOps Engineer Expert (AZ\-400\)
- Be DeepLearning.AI: Machine Learning Engineering for Production (MLOps)
- Have 4\+ years of experience in DevOps, MLOps, or Site Reliability Engineering (SRE), with specific, hands\-on experience managing generative AI deployments in the last 1\-2 years.
- Be deep proficient in building CI/CD pipelines using enterprise tools (Azure DevOps, GitHub Actions, GitLab CI).
- Have hands\-on experience with LLMOps tools and frameworks (e.g., MLflow, LangSmith, PromptFlow, Arize, or similar observability platforms).
- Possess strong Python scripting skills and experience containerizing machine learning or API workloads (Docker, Kubernetes).
- Understand of the API ecosystems for frontier models (OpenAI, Anthropic, Google Vertex AI) and multi\-agent frameworks (LangChain, LangGraph).
- Be familiar with cloud infrastructure (Azure, AWS) and infrastructure\-as\-code principles.
- Be automation\-obsessed: Views manual deployments or manual testing of prompts as an unacceptable operational risk.
- Be financially vigilant: Understands that an infinite loop in a LangGraph agent doesn’t just crash an app—it burns real money through API token costs.
- Be an analytical defender: Deeply curious about *why* a model’s performance degraded in production, relentlessly tracing logs to find the root cause of hallucinations or latency spikes.
Salary Context
This $155K-$195K range is below the median for MLOps Engineer roles in our dataset (median: $209K across 26 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 3,824 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Citrin Cooperman Advisors LLC, 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 76 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($175K) sits 19% below the category median. Disclosed range: $155K to $195K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Citrin Cooperman Advisors LLC AI Hiring
Citrin Cooperman Advisors LLC has 1 open AI role right now. They're hiring across MLOps Engineer. Based in Remote, US. Compensation range: $195K - $195K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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