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About This Role
About Egen:
Egen is a fast-growing and entrepreneurial company with a data-first mindset. We bring together the best engineering talent working with the most advanced technology platforms, including Google Cloud and Salesforce, to help clients drive action and impact through data and insights. We are committed to being a place where the best people choose to work so they can apply their engineering and technology expertise to envision what is next for how data and platforms can change the world for the better. We are dedicated to learning, thrive on solving tough problems, and continually innovate to achieve fast, effective results. If this describes you, we want you on our team.
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About the opportunity
We're looking for an engineer who is equally at home running a GPU training platform and the large-scale model training that runs on it. You'll be embedded with a healthcare and life-sciences AI client that trains foundation models on a large GKE-based H200 and GB200 cluster. The client is reorganizing so that its foundation-model platform and its agent platform teams work more closely together.
The role centres on the training stack and the scientists who use it. Solid infrastructure knowledge and experience are required, but the client already has strong SRE and infrastructure capacity, so running the cluster isn't the focus: you'll partner with that team and understand the training workloads well enough to tune, benchmark, and debug them end to end. You'll work alongside other Egen engineers and client scientists, and you'll own your area outright.
What You Will Do:
Training platform
- Work with the client's infrastructure team on the GKE training platform (GPU node pools, job queueing and scheduling, quota and capacity) from the training workload's side.
- Diagnose and fix platform failures at the root (for example a controller that keeps running out of memory) rather than restarting them.
- Tune multi-node GPU networking (RDMA/GPUDirect, NCCL) for training throughput.
Distributed training
- Run, scale and debug multi-node distributed training with PyTorch (FSDP, DeepSpeed, Megatron or NeMo-style) and Ray Train.
- Profile jobs and remove bottlenecks across compute, memory, input pipelines and interconnect.
Benchmarking and evals
- Design and run performance benchmarks for the cluster and training stack: throughput, scaling efficiency, cost per run.
- Build and maintain model evaluation harnesses so changes to the platform or training recipes are measured, not assumed.
- Instrument training and the platform with telemetry and monitoring, and keep end-to-end pipeline tests running against what users actually run.
Ownership on the client team
- Treat the platform as your own: proactively find issues, plug gaps and propose solutions before anyone asks, keep the team informed of status and risks, and look after the scientists who use it.
- Explain and defend your design decisions to client engineers and scientists in detail.
- Stay effective while the client's priorities and team structure change.
Your Technical Toolkit:
- Core Languages: Mastery of Python and shell scripting
- AI/LLM Ecosystem: Extensive experience with Google Gemini, GPT-4, or LLaMA; deep knowledge of Prompt Engineering and Fine-tuning
- Data & Search: Expertise in Vector Databases (Vertex AI Vector Search, pgvector, etc.) and implementing Semantic Search
- Infrastructure: Hands-on experience with Google Cloud (Vertex AI) and building scalable software architectures
- Frameworks: Proficiency in LangChain, LlamaIndex, or similar orchestration layers
- Mindset: A strong software engineering foundation—you write clean, maintainable code and understand the full SDLC
Basic Qualifications:
- 5+ years of engineering experience, with recent hands-on work on GPU training infrastructure and large-scale training.
- Strong Python software engineering: production-quality code, tests and code review. Coding is central to this role, not an afterthought.
- GKE and GPU scheduling: Kubernetes on Google Cloud with GPU node pools, job queueing (Kueue or similar), and capacity and quota management.
- Distributed PyTorch and Ray Train: has personally run and debugged multi-node training jobs.
- RDMA and GPU networking: GPUDirect/RDMA, NCCL tuning, multi-node GB200 (or H200) topology.
- Benchmarking and evals: has designed performance benchmarks and model evaluations, and used them to make decisions.
- Deep enough understanding to explain how and why a system works, without leaning on AI coding tools to carry the reasoning.
- A track record of visible ownership: present, communicative, and accountable on a demanding team.
Personal Attributes:
Nice to have
- Healthcare or life-sciences data (genomics, clinical, biomedical), and working under HIPAA.
- Observability and telemetry for training platforms: metrics, tracing, alerting, end-to-end pipeline testing.
$159,300 - $219,245 a year This position may be hired at multiple levels. Final leveling is determined during the interview process based on a candidate's experience, skills, and interview outcomes, and the applicable salary range will align with the final level assigned.
Compensation is determined based on factors including experience, expertise, interview performance, and geographic location, where applicable.
Compensation & Benefits:
This role is eligible for our competitive salary and comprehensive benefits package to support your well-being:
- Comprehensive Health Insurance
- Paid Leave (Vacation/PTO)
- Paid Holidays
- Sick Leave
- Parental Leave
- Bereavement Leave
- 401 (k) Employer Match
- Employee Referral Bonuses
Check out our complete list of benefits here - >https://egen.ai/people/#benefits
Important:All roles are subject to standard hiring verification practices, which may include background checks, employment verification, and other relevant checks.
EEO and Accommodations: Egen is an equal opportunity employer and is committed to inclusion, diversity, and equity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veterans’ status, or any other characteristic protected by federal, state, or local laws. Egen will also consider qualified applications with criminal histories, consistent with legal requirements. Egen welcomes and encourages applications from individuals with disabilities. Reasonable accommodations are available for candidates during all aspects of the selection process. Please advise the talent acquisition team if you require accommodations during the interview process. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $159K-$219K range is above the median for MLOps Engineer roles in our dataset (median: $172K 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 11,520 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Egen Solutions Inc, 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 $205,000 based on 171 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($189K) sits 8% below the category median. Disclosed range: $159K to $219K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Egen Solutions Inc AI Hiring
Egen Solutions Inc has 2 open AI roles right now. They're hiring across MLOps Engineer, AI/ML Engineer. Based in Remote, US. Compensation range: $219K - $240K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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