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About This Role
In a world of possibilities, pursue one with endless opportunities. Imagine Next!
At Parsons, you can imagine a career where you thrive, work with exceptional people, and be yourself. Guided by our leadership vision of valuing people, embracing agility, and fostering growth, we cultivate an innovative culture that empowers you to achieve your full potential. Unleash your talent and redefine what’s possible. Job Description:
Parsons is seeking a Senior Software Developer to support our cutting\-edge Drone Armor counter\-unmanned aerial systems (C\-UAS) program. The Senior Developer will design and implement logical, functional software code across multiple programming languages, make informed technology choices for different environments, rapidly diagnose and correct complex software issues in mission\-critical systems, and help design, deploy, and operate machine learning capabilities using modern MLOps practices.
What You'll Be Doing
Advanced Software Design \& Development
- Create logical and functional software code in a variety of programming languages to support Drone Armor capabilities
- Lead the design and implementation of software components, services, and interfaces based on system and mission requirements
- Ensure solutions are robust, secure, maintainable, and aligned with program architecture and coding standards
- Design and implement data and model\-serving services that integrate machine learning components into operational C\-UAS workflows
MLOps, ML Integration \& Lifecycle Management
- Collaborate with data scientists and ML engineers to productionize models, including feature pipelines, inference services, and monitoring
- Implement and maintain end\-to\-end MLOps workflows, including data ingestion, model training, validation, versioning, deployment, and rollback
- Integrate ML pipelines into CI/CD processes to enable automated testing, packaging, and deployment of models and ML\-enabled services
- Establish observability for ML systems (data drift, model performance, latency, accuracy) and support continuous model improvement
- Ensure ML systems meet mission\-critical requirements for reliability, explainability, security, and compliance in DoD/defense environments
Technology Evaluation \& Trade\-Offs
- Understand and articulate the benefits and risks associated with different coding languages and frameworks in various functional environments
- Recommend appropriate languages, tools, and design patterns based on performance, security, maintainability, and integration needs
- Evaluate and recommend ML frameworks, data processing tools, and MLOps platforms (e.g., experiment tracking, model registries, feature stores)
- Provide technical guidance to developers on language and framework selection, coding practices, architectural decisions, and ML/MLOps integration strategies
Troubleshooting, Debugging \& Quality
- React to software problems quickly and effectively, correcting code and related configurations as necessary
- Debug complex issues across multiple layers (application, service, interface, data) and environments (development, integration, field)
- Diagnose and resolve issues specific to ML systems, including model\-serving performance, data quality, and pipeline failures
- Support and refine unit, integration, system\-level, and ML\-specific tests (e.g., data validation, model performance checks) to validate functionality and prevent regressions
Leadership \& Collaboration
- Serve as a senior technical resource within the development team, mentoring junior and mid\-level developers
- Coach team members on best practices for integrating ML components and MLOps into existing software architectures
- Collaborate with systems engineers, test engineers, data scientists, ML engineers, and field personnel to resolve issues and improve system performance
- Contribute to technical reviews, design walkthroughs, and continuous improvement of development and MLOps practices
What Required Skills You'll Bring
Education
- Bachelor’s degree in Computer Science, Electronics Engineering, or other engineering or technical discipline is required with 5 years of experience OR
- 8 years of relevant software development experience may be substituted for education
Experience
- Experience creating logical and functional software code in multiple programming languages
- Experience understanding and clearly articulating the benefits and risks of different coding languages in different functional environments
- Experience reacting to software problems and correcting programs as necessary in complex or mission\-critical systems
- Experience deploying, operating, or supporting machine learning models in production environments (MLOps), including monitoring and maintaining ML\-enabled services
Technical Competencies
- Proficiency in one or more modern programming languages (e.g., Python, C\+\+, Java, C\#, Go, or similar), with working knowledge of others
- Strong grasp of software engineering best practices, including design patterns, code reviews, version control, and CI/CD workflows
- Demonstrated ability to troubleshoot and resolve complex software defects efficiently
- Experience integrating ML workflows into software systems (e.g., REST/gRPC model services, batch inference, streaming pipelines)
- Familiarity with MLOps concepts and tools such as:
+ CI/CD for ML (e.g., automated training and deployment pipelines)
+ Model versioning and registries
+ Data and model monitoring, including drift and performance tracking
- Strong analytical and communication skills, capable of explaining technical and ML\-related trade\-offs to both technical and non\-technical stakeholders
Security \& Citizenship
- Must be a US Citizen
- SECRET security clearance
What Desired Skills You'll Bring
Advanced Education \& Certifications
- Bachelor’s or higher degree in Computer Science, Computer Engineering, or related discipline
- Relevant certifications in software architecture, cloud platforms, DevSecOps, or MLOps/ML engineering
Specialized Experience
- Experience supporting DoD, defense, or C\-UAS\-related software systems
- Experience with distributed, real\-time, or high\-availability systems
- Experience deploying and managing ML models in constrained, real\-time, or edge environments (e.g., forward\-deployed, on\-platform, or tactical systems)
Additional Technical Skills
- Experience with containerization (Docker), orchestration (Kubernetes), and cloud\-native development
- Experience with ML frameworks and ecosystems (e.g., TensorFlow, PyTorch, scikit\-learn, ONNX, or similar) and associated deployment stacks
- Familiarity with feature stores, experiment tracking tools, and model registries as part of an MLOps workflow
- Familiarity with Agile/Scrum methodologies and modern issue tracking/ALM tools
Security Clearance Requirement:
An active Secret security clearance is required for this position.
This position is part of our Federal Solutions team.
The Federal Solutions segment delivers resources to our US government customers that ensure the success of missions around the globe. Our intelligent employees drive the state of the art as they provide services and solutions in the areas of defense, security, intelligence, infrastructure, and environmental. We promote a culture of excellence and close\-knit teams that take pride in delivering, protecting, and sustaining our nation's most critical assets, from Earth to cyberspace. Throughout the company, our people are anticipating what’s next to deliver the solutions our customers need now.
Salary Range: $103,500\.00 \- $181,100\.00
We value our employees and want our employees to take care of their overall wellbeing, which is why we offer best\-in\-class benefits such as medical, dental, vision, paid time off, Employee Stock Ownership Plan (ESOP), 401(k), life insurance, flexible work schedules, and holidays to fit your busy lifestyle!
Parsons is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, veteran status or any other protected status.
We truly invest and care about our employee’s wellbeing and provide endless growth opportunities as the sky is the limit, so aim for the stars! Imagine next and join the Parsons quest—APPLY TODAY!
Parsons is aware of fraudulent recruitment practices. To learn more about recruitment fraud and how to report it, please refer to https://www.parsons.com/fraudulent\-recruitment/.
COMPETITIVE BENEFIT OFFERINGS
Financial Wellness
We care about your financial wellbeing. Parsons offers competitive pay and retirement plans to help you build wealth for the future while giving you the flexibility to diversify your investments.
Work Life Harmony
Balance in life is important and time away from the office is imperative to allow you to refresh and focus your attention on the things that matter to you. Parsons supports your time away by providing paid time off and paid flexible holidays.
Career Development
We are committed to fostering the personal and professional growth of our employees. Develop and advance yourself though our comprehensive training, educational and mentorship programs.
Veteran Support
We provide Industry leading benefits to support veterans and active\-duty members to provide security for you and your family by offering robust leave and benefits; including paid active\-duty military leave and paid time off when transitioning back to civilian life.
Mind \& Body
At Parsons we inspire healthier habits, heathier minds, and a healthier you through our wellness program. Participate in our weekly Meditation Mondays and Wellness Wednesdays. Wellness, at Parsons, is more than just your annual checkup.
Health
Health is not a one size fits all. At Parsons, we offer a robust Employee Assistance Program as well as comprehensive medical, dental and vision plans through large, national carriers with the choice of regional PPO, HDHP, or HMO networks.
Salary Context
This $103K-$181K range is below the median for MLOps Engineer roles in our dataset (median: $168K across 34 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 4,317 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Parsons, 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. This role's midpoint ($142K) sits 30% below the category median. Disclosed range: $103K to $181K.
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.
Parsons AI Hiring
Parsons has 8 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer, Data Scientist, MLOps Engineer. Positions span Baltimore, MD, US, Remote, US, Annapolis Junction, MD, US. Compensation range: $181K - $266K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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.
Frequently Asked Questions
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