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About This Role
We are a growing information technology company that offers its employees a culture of success, the chance to work on revolutionary federal IT infrastructure, and the opportunity to grow alongside cutting\-edge technology that is reshaping the industry. We are seeking forward\-thinking candidates that have strong experience in operational support and can help take to the next level in a proactive stance.
Chameleon Integrated Services has expertise in operations management, quality systems, data operations and cybersecurity. We secure some of the most sensitive data for the Department of Defense and for other U.S. federal government agencies. We are known for the great care we take with clients and employees, and we believe in promoting from within.
We offer a Full Benefits package including:* Competitive Employee Health Insurance options including dental
- 100% company paid vision plan
- 401K plan with generous company match and no vesting period
- 100% company paid life insurance
- 100% company paid long and short\-term disability insurance
- Training allowance
- PTO and more
Azure Data and MLOps Engineer
Role: Build the Azure data pipelines, system integrations, deployment mechanisms, and operational MLOps foundation supporting SEA dashboards, applications, and AI/ML models.
Task 4 requires compliant data pipelines, API or service\-based integrations, data ingestion, transformation, synchronization, Azure\-native services, security controls, integration architecture, data\-flow documentation, and validated connections.
Requirements:* Active Secret clearance.
- Hands\-on Azure data\-engineering experience.
- Strong SQL and Python capability.
- Experience building production ETL/ELT pipelines and API\-based integrations.
- Experience deploying or operationalizing machine\-learning models.
- Experience implementing identity, access, secrets, logging, and monitoring in cloud environments.
- Bachelor’s degree in computer science, information systems, engineering, data engineering, or a related discipline; equivalent experience may substitute.
- Six or more years in data engineering, cloud engineering, platform engineering, or software integration.
- Three or more years using Microsoft Azure.
- Experience with at least two major Azure data technologies, such as:
+ Azure Data Factory.
+ Azure Synapse Analytics.
+ Azure Databricks.
+ Azure SQL.
+ Data Lake Storage.
+ Microsoft Fabric where relevant.
- Experience developing REST API, services, databases, file\-based, or event\-driven integrations.
- Experience with Git, Azure DevOps, or comparable CI/CD tooling.
- Experience with data validation, schema management, error handling, logging, and recovery.
- Experience deploying models as endpoints, containers, batch jobs, or integrated services.
Strong preferences* Azure Government, cARMY, IL4/IL5/IL6, GCC High, or another restricted Government environment.
- Azure Machine Learning, Azure OpenAI, MLflow, Docker, Kubernetes, or model\-serving technologies.
- Entra ID, managed identities, Key Vault, RBAC, private endpoints, network controls, and secure secrets management.
- GCSS\-Army, SAP, ERP, Army logistics, or DoD system integration.
- Data lineage, metadata, governance, and architecture documentation.
- Power BI gateways, semantic models, or Power Platform integration.
- Experience supporting classified data.
Principal responsibilities* Design SEA data\-ingestion and integration architecture.
- Connect SEA to additional authoritative Army systems.
- Build and maintain pipelines that ingest, transform, reconcile, and synchronize data.
- Develop API and service integrations using approved Army patterns.
- Support structured, semi\-structured, and document\-based data.
- Implement model\-development and deployment environments.
- Establish CI/CD, versioning, model packaging, release, rollback, and monitoring.
- Configure identity, secrets, logging, alerting, and security controls.
- Diagnose data latency, quality, pipeline, model\-endpoint, and integration failures.
- Produce architecture diagrams, interface descriptions, data\-flow diagrams, and operational procedures.
- Support Power BI and Power Apps data connections.
Location and Work Expectations:* Ability to work remotely during normal Government working hours and participate in daily status, requirements, development, and technical\-review sessions.
- Ability to travel to Fort Lee, Virginia, at least three times annually and report onsite more frequently if directed.
*“We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status”*
*Texting Privacy Policy*
- Message type: Informational; you will receive text messages regarding your application and potentially regarding interview scheduling.
- No mobile information will be shared with third parties/affiliates for marketing/promotional purposes.
- Message frequency will vary depending on the application process.Msg \& data rates may apply.
- OPT out at any time by texting "Stop".
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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 Chameleon Integrated Services, 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. Mid-level AI roles across all categories have a median of $200,000.
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.
Chameleon Integrated Services AI Hiring
Chameleon Integrated Services has 2 open AI roles right now. They're hiring across MLOps Engineer, Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 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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