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About This Role
Harnessing Technology to Improve Financial Stewardship for the Welfare, Defense, and Security of Our Nation
Blake Willson Group (BWG) unites deep domain experts with technologists who leverage industry-leading financial management solutions to address the most critical mission objectives. Headquartered in the National Capital Region, the firm delivers measurable outcomes through technology-forward strategies and advanced solutions that drive mission success.
Blake Willson Group has a distinguished track record of exceptional performance, achieving operational efficiencies that allow our clients to do more with less. BWG has earned the confidence of its clients by consistently exceeding expectations through its unwavering commitment to best value solutions, implemented with speed.
Job Location:
This role is 100% remote.
Clearance:
Must be currently authorized to work in the United States on a full-time basis and have the ability to obtain a Public Trust Clearance.
Job Description:
Blake Willson Group is seeking a Senior Cloud, Data & AI Engineer to lead the architecture, development, and delivery of innovative cloud, data, process intelligence, and AI solutions. This role will own technical delivery across AWS GovCloud and Celonis environments, with a focus on building secure, scalable, and reusable solutions for federal clients. The ideal candidate brings deep software and data engineering expertise, hands-on AWS experience, and practical experience with Celonis and AI/agentic technologies. In this position, you will also:
- Architect, implement, and operate cloud-native services on AWS, including API Gateway, Lambda, Fargate, DynamoDB, SQS, S3, KMS, IAM, and CloudWatch, using infrastructure as code, CI/CD, observability, security, and tenancy controls.
- Build data ingestion, transformation, and API components for large-scale, multi-source data platforms, including ETL and streaming pipelines, authentication and authorization, and data quality validation.
- Develop and extend Celonis solutions, including Data Connections, Data Integration transformations, object-centric data models, event logs, Knowledge Models, PQL metrics, Studio Views, and Action Flows.
- Design and build agentic AI workflows using managed LLM services such as Amazon Bedrock and Azure OpenAI, as well as Celonis AI capabilities, with appropriate evaluations, guardrails, and traceability.
- Own system architecture and technical direction for Innovation Lab products, including design decisions, code reviews, engineering standards, testing, security, and observability.
- Lead end-to-end technical delivery of the A-123 Continuous Controls Monitoring solution and additional Innovation Lab products from prototype through pilot and production deployment.
- Package solutions for reuse across federal agencies through parameterized configuration, automated deployment, versioning, and reference architectures.
- Support capability demonstrations and post-award implementation activities, including source system mapping, environment configuration, and deployment into agency and FedRAMP-authorized cloud environments.
- Collaborate with Celonis and AWS partner engineering teams on solution certification, marketplace listings, and partner program requirements.
- Establish scalable engineering practices that improve reliability, data accuracy, deployment efficiency, and the ability to rapidly stand up solutions for new agency environments.
- Maintain a focus on measurable business impact, including reducing manual control testing, audit, and review hours and increasing the use of Innovation Lab solutions across client engagements, demonstrations, and proposals.
Required Skills:
- Bachelor's degree in Computer Science, Engineering, Information Technology, Business, or a related field.
- 7+ years of professional software or data platform engineering experience, with a proven track record of building and operating production systems at scale.
- 5+ years of experience with Python and SQL, including deep hands-on AWS experience across serverless compute, managed data stores, messaging, IAM, encryption, and infrastructure as code using Terraform, CDK, or CloudFormation.
- 5+ years of experience with cloud-native data and AI solutions, including hands-on Celonis experience with process mining, data models, Knowledge Models, and PQL, as well as experience building or integrating LLM-based or agentic systems.
Desired Skills:
- Prior engineering experience with Celonis, AWS, or a Celonis implementation partner.
- AWS Certified Solutions Architect and/or Celonis certifications such as Data Engineer, Process Intelligence Developer, or Automation.
- Experience with AWS GovCloud, FedRAMP, NIST 800-53, and Authority to Operate processes for cloud-hosted analytics and AI systems.
- Experience with object-centric process mining, Celonis ML Workbench, Blueprint API, and Marketplace packaging.
- Federal financial management domain knowledge, including OMB Circular A-123, internal controls, PIIA, improper payments, grants management, or financial close and reporting.
- Experience with Azure data and AI services, OneStream, Workiva, or Power BI.
- Experience with Agile delivery methodologies, including Scrum or Kanban, and strong communication skills with executive and technical audiences.
The statements above describe the general nature and level of work anticipated for this role. They are not intended to be an exhaustive list of all duties, responsibilities, or skills required. Blake Willson Group reserves the right to modify, assign, or add job-related responsibilities as business needs require. Where feasible, reasonable accommodations may be provided for individuals to perform essential job functions.
Blake Willson Group is an Equal Employment Opportunity (EEO) employer and is committed to maintaining a professional, respectful, and harassment-free workplace. All employment decisions are based on business needs, qualifications, and merit. We comply with all applicable federal, state, and local employment laws and do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status, genetic information, or any other legally protected status. Blake Willson Group prohibits unlawful discrimination, harassment, and retaliation.
Blake Willson Group complies with federal equal employment opportunity requirements. The "Know Your Rights: Workplace Discrimination Is Illegal" poster is available to applicants and employees. View the official poster here: Know Your Rights: Workplace discrimination is illegal
If you require a reasonable accommodation during the application process, please contact us at 202-381-0603, Ext. 3.
Blake Willson Group participates in E-Verify to confirm employment eligibility and will provide the federal government with your Form I-9 information to verify authorization to work in the United States.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Blake Willson Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
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.
Blake Willson Group AI Hiring
Blake Willson Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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