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About This Role
We are seeking an experienced MLOps Engineer to automate secure deployment, evaluation, monitoring, and lifecycle management for on-premises AI services for a Federal data analytics and AI modernization initiative.
The team will deliver a secure, scalable platform that integrates structured and unstructured data, provides data visualization and traceable AI-assisted analytics, and gives examiners centralized tools for search, review, monitoring, and decision support. The platform will support professional judgment and will not replace authoritative agency financial or grants-management systems or execute financial transactions.
Analytica has been recognized by Inc. Magazine as the fastest-growing private US small business. We work with U.S. government customers in health, civilian, and national security missions. As a core member you’ll work with a diverse team of professionals to solve matters, architect nuisances, and come up with alternatives. We offer competitive compensation with opportunities for bonuses, employer paid health care, training and development funds, and 401k match.
Key Technical Responsibilities
- Design, develop, and maintain data pipelines and ETL/ELT workflows supporting machine learning, NLP, retrieval, and advanced analytics.
- Prepare, transform, curate, and validate structured and unstructured grants and financial data for AI/ML applications.
- Perform feature engineering, document processing, chunking, embeddings generation, and dataset preparation for AI-assisted analytics and retrieval workflows.
- Develop reproducible data transformations and workflows using Python, SQL, and applicable data engineering technologies.
- Implement and maintain data quality, validation, provenance, lineage, versioning, metadata, and source traceability throughout the data lifecycle.
- Prepare and integrate data for search, retrieval-augmented generation (RAG), visualization, analytics, and AI-assisted decision support.
- Troubleshoot data pipeline, transformation, integration, and data-quality issues across development and testing environments.
- Collaborate with multidisciplinary data engineering, data science, software engineering, and AI/ML teams during iterative development and testing.
- Develop technical documentation covering data architecture, pipelines, transformations, schemas, features, embeddings, dependencies, and operational procedures.
- Support deployment and operation of data engineering capabilities within secure Federal, on-premises, cloud, or hybrid environments.
- Ensure data engineering solutions comply with Federal security, privacy, accessibility, records-management, data-ownership, and governance requirements.
Required Technical Qualifications
- Demonstrated experience building, integrating, and operating data pipelines for machine learning, NLP, information retrieval, or advanced analytics.
- Hands-on experience with feature engineering, document processing, embeddings, curated datasets, data transformation, and reproducible data workflows.
- Strong proficiency in Python and SQL for data engineering, data transformation, automation, and model-support workflows.
- Experience working with structured and unstructured data and preparing data for AI/ML or analytics applications.
- Experience implementing data quality controls, data validation, provenance, metadata, versioning, lineage, and source traceability for AI/ML or data-intensive systems.
- Experience troubleshooting and optimizing data pipelines, integrations, transformations, and data-processing workflows.
- Ability to develop clear technical documentation covering data pipelines, schemas, transformations, dependencies, and operational processes.
- Strong technical communication, problem-solving, and cross-functional collaboration skills.
- U.S. citizenship and ability to obtain and maintain Top Secret eligibility, as required for contractor personnel supporting the effort.
- Active Top Secret clearance highly preferred.
Preferred Technical Qualifications
- Experience supporting Federal data, financial management, budget execution, grants management, payment systems, award oversight, financial reporting, or financial stewardship data.
- Experience delivering data engineering or AI/ML solutions in secure Federal, on-premises, cloud, or hybrid environments.
- Experience with Linux-based environments.
- Experience with PostgreSQL and/or Microsoft SQL Server.
- Experience working with Java and/or .NET applications and integrations.
- Familiarity with Jenkins and CI/CD pipelines.
- Experience with self-hosted Azure DevOps.
- Experience with Kubernetes and/or Rancher.
- Familiarity with open-source AI/ML models and supporting infrastructure.
- Experience supporting RAG, vector search, semantic search, embeddings pipelines, or other AI retrieval architectures.
- Familiarity with Federal data governance, security, privacy, and compliance requirements.
About ANALYTICA: Analytica is a leading consulting and information technology solutions provider to public sector organizations supporting health, civilian, and national security missions. Founded in 2009 and headquartered in Bethesda, MD, the company is an established SBA small business that has been recognized by Inc. Magazine each of the past three years as one of the 250 fastest-growing companies in the U.S. Analytica specializes in providing software and systems engineering, information management, analytics & visualization, agile project management, and management consulting services. The company is appraised by the Software Engineering Institute (SEI) at CMMI® Maturity Level 3 and is an ISO 9001:2008 certified provider. Analytica LLC is an Equal Opportunity Employer. We are committed to providing equal employment opportunities to all individuals, regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or any other characteristic protected by applicable federal, state, or local law. As a federal contractor, we comply with the Vietnam Era Veterans' Readjustment Assistance Act (VEVRAA) and take affirmative action to employ and advance in employment qualified protected veterans. We ensure that all employment decisions are based on merit, qualifications, and business needs. We prohibit discrimination and harassment of any kind. Analytica LLC also provides reasonable accommodations to applicants and employees with disabilities, in accordance with applicable law.
To enhance efficiency, fairness, and accuracy, Analytica may use AI-assisted tools to support certain aspects of our hiring process.
- Application Review: AI tools may help identify skills and experiences relevant to the role.
- Interview Support: AI-powered notetaking tools may be used during interviews to document discussions and summarize key points.
These tools are used to assist our team. All hiring decisions are made by Analytica recruiters and hiring managers.
By submitting an application, you acknowledge that AI-assisted tools may be used to support parts of the application and interview process.
When receiving email communication from Analytica, please ensure that the email domain is analytica.net to verify its authenticity.
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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 9,653 AI roles we're tracking, MLOps Engineer positions make up 2% of the market. At Analytica, 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 151 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Analytica AI Hiring
Analytica has 2 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer. Based in Bethesda, MD, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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