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About This Role
About Us:
Zeitview is the leading intelligent aerial imaging company for high\-value infrastructure, providing businesses with actionable, real\-time insights to recover revenue, reduce risk and improve build quality. We serve customers in the solar, wind, insurance, construction, real estate, and critical infrastructure industries. Trusted by the largest enterprises in the world, Zeitview is active in over 70 countries. Our mission is to accelerate the global transition to renewable energy and sustainable infrastructure through advanced inspection solutions. Take a look at our latest achievements here!
About the Role:
As the Senior MLOps Engineer I, you will help turn the models built by our ML Scientists, Data Scientists, and Perception Engineers into reliable, production\-grade services. You'll work on the infrastructure, pipelines, and tooling that take a model or an LLM/agent\-backed workflow from a research notebook to a fully monitored deployment running across multiple industry verticals, including our model registry, deployment pipelines, and the cloud infrastructure our AI/ML platform depends on.
This role sits at the intersection of R\&D, Software Engineering, and DevOps. You will work daily with our R\&D team to understand what a model needs to run in production (compute, data inputs, versioning, post\-processing), and you'll partner closely with the Platform and DevOps teams to provision the infrastructure, permissions, and deployment pathways that make it possible. You'll also contribute to broader automation initiatives, helping provide the deployment visibility and pipeline reliability that let R\&D, Software, Product, and Ops teams move in lockstep.
The day\-to\-day will include maintaining and extending our model registry, building and debugging deployment pipelines and cloud infrastructure, and setting up model and pipeline monitoring and testing. You will also troubleshoot issues, such as failed deployments, permissions errors, or inconsistent environments. You'll also help shape and document standards for how models move from staging to production. Perhaps most importantly, you will serve as a key communicator ensuring R\&D goals and challenges are well understood by Software Engineering and DevOps teams.
Responsibilities:
- Partner with Scientists: Work directly and iteratively with ML Scientists, Data Scientists, and Perception Engineers to translate experimental, research\-oriented code into dependable, scalable production services without slowing down their research velocity.
- Cross\-Functional Collaboration: Coordinate with DevOps and Software Engineering teams on infrastructure requests and shared data pipeline needs, and support broader automation initiatives and team goals.
- Model Registry, Deployment \& Release Management: Maintain and improve model registry and deployment pipelines, and help implement safer release practices (e.g., shadow deployments, rollback procedures) to reduce risk.
- Cloud Infrastructure \& CI/CD: Build, maintain, and troubleshoot cloud infrastructure and CI/CD pipelines that ML workloads run on, working closely with Engineering and DevOps teams on shared tooling, infrastructure\-as\-code, and cost optimization for compute\-heavy workloads.
- Monitoring, Drift \& Reproducibility: Implement monitoring and observability for models and pipelines in production, help R\&D track model performance and drift over time, and support experiment tracking and dataset/model versioning.
- Ongoing Maintenance \& Platform Support: Keep deployed ML systems healthy over time with dependency and infrastructure upgrades, capacity and cost management, data pipeline upkeep, and retraining or redeployment support, and extend support as needs evolve.
- Standards \& Documentation: Help define and document conventions for model versioning, deployment promotion, and model documentation/lineage, and build tools to allow scientists and engineers to self\-serve.
Qualifications:
The following describes the qualifications for this position. Successful candidates are expected to meet most, but not all, of these requirements.
- Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or a related field; typically 4\+ years of professional experience in MLOps, ML platform engineering, or infrastructure engineering supporting machine learning teams.
- Solid, applied knowledge of MLOps practices, with the ability to work independently across varied production scenarios and escalate only genuinely complex or ambiguous problems.
- Demonstrated experience working directly with researchers or ML scientists. You understand research workflows and can translate them into reliable services and productionized models without becoming a bottleneck. You serve as a key link, communicating R\&D goals and challenges to Software Engineering and DevOps teams.
- Strong Python skills and solid software engineering fundamentals (testing, code review, version control)
- Hands\-on experience with a major cloud platform (e.g., AWS), infrastructure\-as\-code (Terraform), CI/CD tooling (Github Actions), and containerization/orchestration (e.g., Docker, Kubernetes)
- Experience building and operating production ML pipelines and model registries, including model versioning and safer release practices (e.g., canary deployments, rollbacks) across environments, as well as coordinating moderately complex, cross\-functional infrastructure or deployment projects.
- Experience building feedback loops from production back into training data, capturing human corrections as labels and turning retraining into a repeatable pipeline. Familiarity with experiment tracking, dataset/model versioning, and model documentation practices that support reproducible, auditable ML workflows is a plus.
- Familiarity with computer vision or geospatial ML pipelines
- \[Nice to have] Experience operating LLM/Agentic systems in production, evaluation harness, prompt/tool/retrieval versioning, tracing, token cost optimization
- \[Nice to have] Experience building data pipelines against relational databases (e.g. PostgreSQL) and API/GraphQL data layers (e.g., Hasura), and integrating external/third\-party APIs into production workflows.
What’s Included:
- Feel great about your work as you join a leading mission\-driven intelligent aerial imaging company \- our goal is to accelerate the global transition to renewable energy and sustainable infrastructure, and you personally will play a large part in making this happen!
- Base salary range of $170,000 \- $180,000 USD
- Target annual bonus
- Eligibility for stock options
- Your choice of multiple medical insurance plans, including options with an HSA and 100% coverage of the premium for yourself and your dependents
- 100% paid dental and vision insurance
- Unlimited PTO: We mean it when we say we prioritize work\-life balance and mental health
- Autonomy and upward mobility
- Diverse, equitable, and inclusive culture: a place where your voice matters
This role has a base salary range of $170,000 \- $180,000 USD, plus a target annual bonus and eligibility for stock options. Actual compensation may vary based on experience, skills, and location within the USA.
*Zeitview is proud to be an equal opportunity employer. At Zeitview, we believe in cultivating an environment where our team members can bring their authentic, whole selves to work. Encouraging identity and belonging is one of the many aspects of our culture that makes us stronger as an organization and drives innovation. We are committed to building and delivering a diverse, inclusive, and equitable workforce that includes age, color, sex, disability, national origin, race, religion or veteran status, that is representative of the world around us, where all individuals are treated with respect and dignity \- and to act swiftly if this value is ever threatened. We are constantly striving to be better, and we continue to take strategic steps to advance representation.*
*We also provide reasonable accommodation for qualified individuals with disabilities and for seriously held religious beliefs in accordance with applicable law.*
Salary Context
This $170K-$180K range is above the median for MLOps Engineer roles in our dataset (median: $177K across 20 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 3,708 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Zeitview, 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 $220,000 based on 47 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($175K) sits 20% below the category median. Disclosed range: $170K to $180K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Zeitview AI Hiring
Zeitview has 1 open AI role right now. They're hiring across MLOps Engineer. Based in Boston, MA, US. Compensation range: $180K - $180K.
Location Context
AI roles in Boston pay a median of $210,000 across 97 tracked positions. That's 3% below 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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