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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 pro\-active 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.
Principal Azure Data Platform Architect / Databricks \& MLOps Lead
Position Overview* Position Type: Part\-Time Consultant / Technical Architecture Lead
- Target Allocation: 22–26 hours/week average (Note: Workload is highest during the initial architecture build\-out, legacy environment integration, automated pipeline deployment, multi\-agency onboarding phases, and operational readiness reviews).
- Location: Remote (U.S. Based) with periodic travel to Tallahassee, FL as required.
- Cold\-Start Baseline: The RFQ strictly mandates that all engineering, configuration, and testing occur entirely within State\-provided environments. No vendor\-hosted development or proprietary runtimes are permitted.
Chameleon is seeking a Principal Azure Data Platform Architect / Databricks \& MLOps Lead to engineer the foundational infrastructure for a high\-visibility contract with the Florida Office of the Chief Inspector General (OCIG). In this role, you will design, deploy, and automate the secure, multi\-agency cloud data platform transforming a data analytics Proof of Concept (POC) into an enterprise\-ready Decision Intelligence Platform.
This platform will unify statewide oversight, tracking abnormal spending patterns, contract vulnerabilities, and fraud/waste/abuse risks across up to 35 state agencies. Because this is a high\-visibility, firm\-fixed\-price (FFP) state government contract, you will maintain absolute technical accountability for establishing an infrastructure that guarantees a 99% or greater pipeline run success rate and a 99\.5% or greater overall system availability rating.
Principal Responsibilities* Multi\-Environment Architecture Ownership: Take complete engineering ownership of the core system architecture across four distinct, logically isolated State environments: Development (DEV), Test/QA (TEST), User Acceptance Testing (UAT), and Production (PROD).
- Cloud Data Foundation Build\-Out: Deploy, configure, and manage the State\-owned infrastructure utilizing Azure Data Lake Storage Gen2 (ADLS Gen2\) and integrated Databricks workspaces.
- Analytical Pipeline Orchestration: Establish robust, reproducible patterns for automated data ingestion, incremental/historical loading, canonical schema processing, business rule execution, and machine learning scoring engines.
- Automated Reliability Controls: Build and implement data pipeline monitoring to continuously demonstrate a 99% or greater successful run rate, factoring in real\-time alerting for data anomalies, pipeline failures, or performance degradations.
- Promotion \& Release Management: Engineer automated version control, code rollback, environment promotion paths, and disaster recovery mechanics that align strictly with specified Recovery Time Objective (RTO) and Recovery Point Objective (RPO) targets.
- Forensic Audit Traceability: Implement exhaustive logging, error handling, retry logic, and metadata management to preserve comprehensive source\-to\-target data lineage, providing the clear operational evidence required for independent OCIG verification testing.
- Technical Agency Onboarding: Coordinate and execute the technical onboarding pipelines for an initial wave of 8–10 state agencies, establishing secure file transfer protocols and resolving cross\-system schema differences.
- State Ownership Handover: Package and document all system components, mapping templates, code notebooks, and configuration files into a fully editable, non\-proprietary State Ownership and Transition Package.
Required Qualifications* Experience Baseline: 10\+ years of comprehensive enterprise software or big data engineering experience.
- Cloud Depth: 5\+ years of dedicated, hands\-on Azure cloud architecture and data platform deployment work.
- Platform Toolkit: Extensive production\-level experience configuring Azure Data Lake Storage Gen2 (ADLS Gen2\), Azure Databricks, and Azure Data Factory (or equivalent enterprise orchestration tools).
- Languages: Advanced engineering proficiency in Python/PySpark and SQL for streaming and batch processing.
- Pipeline Frameworks: Expert knowledge designing Raw, Silver, and Gold data layer architectures, managing schema evolution, data quality validation, and ledger reconciliation scripts.
- Ingestion Vectors: Proven success establishing API, database, and flat file\-based ingestion pathways across highly disparate legacy architectures.
- Cloud Security \& Identity: Advanced hands\-on mastery of Azure security controls, including Entra ID, role\-based access control (RBAC) alignment to least\-privilege principles, managed identities, and Key Vault configuration.
- DevOps \& Monitoring: Strong background building CI/CD deployment pipelines, version\-controlled repositories, and production monitoring dashboards.
- MLOps Integration: Documented experience supporting machine learning scoring workflows, model deployment automation, or data pipeline drift detection systems.
- Government Context: Proven history delivering technology, cloud architecture, or data infrastructure projects for local, state, military, or federal government entities.
- Vetting \& Location: Must be a U.S.\-based citizen or resident. Must be able to successfully clear an FDLE Level II background screening (including fingerprinting) within 5 business days of contract award.
Strong Preferences* Direct experience deploying and managing architectures within secure Azure Government cloud environments.
- Hands\-on configuration experience utilizing MLflow, Delta Lake, or automated data lineage/metadata cataloging repositories.
- Background managing technical integrations with Microsoft Power BI workspaces and handling specialized .pbix configuration templates.
- Deep technical familiarity with NIST standards (SP 800\-53/171\), FedRAMP High control baselines, CJIS, or HIPAA data protection guidelines.
- Prior experience establishing standardized multi\-agency or cross\-department state/federal data exchange models.
MANDATORY RESUME FORMATTING INSTRUCTIONS
The State of Florida strictly evaluates and verifies all named staff experience for this contract. Generic resumes that only list generalized technical summaries or generic cloud keyword lists will be automatically rejected.
To be considered for this role, your resume must explicitly detail the following metadata for your past contract positions:* The Government Customer: Explicitly name the agency and the cloud infrastructure context (e.g., State Department, Military, Federal Agency).
- Architecture Scale \& Complexity: Specify the exact dataset volumes processed, the number of distinct environments managed, and the precise project duration.
- Personal Engineering Contribution: Detail exactly what you personally built, scripted, or configured (e.g., *"Coded PySpark reconciliation scripts," "Configured Azure RBAC least\-privilege policies"*).
- System Deployment Status: Explicitly prove that your architectures reached true production, steady\-state, or operational status rather than just theoretical design.
- Quantifiable Performance Metrics: Provide the precise metrics achieved under your management, such as percentage of pipeline run success rates, system availability uptime, data completeness markers, or page load response times.
*“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*
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- 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 4,317 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 $203,000 based on 85 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 $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Chameleon Integrated Services AI Hiring
Chameleon Integrated Services has 2 open AI roles right now. They're hiring across Data Scientist, MLOps Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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.
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