Interested in this AI/ML Engineer role at BLN24?
Apply Now →Skills & Technologies
About This Role
Job Title: Data and AI Engineer
Company: BLN24
About Us: We find strength in teamwork-a better you is a better us
BLN24 is an award-winning Management Consulting Firm that supports the U.S. Federal Government in successfully achieving their mission and goals. Our service and solutions delivery start with understanding each client’s end-state, and then seamlessly integrating within each Agency’s organization to improve and enhance strategic and technical operations and deployments.
Overview
Be part of a transformative digital mission supporting the National Weather Service (NWS) at NOAA. As a Data and AI Engineer, you will help architect and implement advanced data pipelines, scalable analytics frameworks, and intelligent automation capabilities to modernize legacy applications, empower forecasters, and deliver critical insights that protect lives and property.
This role directly supports NOAA’s multi-phase modernization effort, including the enhancement of the Storm Event Database (SED), optimization of storm reporting systems, and evaluation of Generative Artificial Intelligence (AI) to drive operational efficiency across weather data workflows.
Key Responsibilities:
Data Architecture & Engineering* Design and build scalable, cloud-compatible data pipelines to support modernization of the Storm Event Database (SED) and storm reporting applications.
- Develop Extract, Transform, Load (ETL) solutions for ingesting structured and unstructured meteorological data.
- Ensure data quality, performance, and accessibility through normalization, deduplication, and schema optimization.
- Enable advanced analytics by organizing historical and real-time weather event data for use in forecasting, alerting, and impact analysis.
AI/ML Integration & Generative AI Exploration* Evaluate, prototype, and integrate Generative AI technologies (e.g., LLMs, synthetic data, intelligent assistants) to enhance NOAA workflows and software development processes.
- Identify and document suitable use cases for AI acceleration in operational settings, such as text generation for weather summaries or developer code assist tools.
- Collaborate with DevSecOps and cyber teams to ensure AI systems meet federal guidelines for security, privacy, and ethical use.
Analytics & Insights Development* Partner with NOAA analysts, meteorologists, and engineers to define analytical requirements that support mission outcomes.
- Deliver predictive models, trend analyses, and visual dashboards that help NWS better communicate weather phenomena to the public and decision-makers.
- Optimize query logic and data warehouse structure to improve accessibility and speed for internal NOAA users and external stakeholders.
Agile Delivery & Collaboration* Participate in Agile sprints to prioritize backlog items, demo technical solutions, and continuously improve delivery outcomes.
- Collaborate with Full Stack Developers and Cloud Architects to integrate data services into modernized applications.
- Contribute to documentation, testing, and configuration management in alignment with federal DevSecOps standards.
Required Qualifications* Bachelor’s degree in Computer Science, Data Engineering, Statistics, or related technical field
- 5+ years of experience in data engineering, analytics, or machine learning development
- Proficiency in Python, SQL, and one or more data frameworks (e.g., Apache Spark, Airflow, Kafka)
- Experience working with cloud data platforms (e.g., AWS, Azure, or GCP) and data lakes/warehouses
- Familiarity with model development and deployment (e.g., MLflow, TensorFlow, Hugging Face Transformers)
- U.S. Citizenship and eligibility for Public Trust clearance
Preferred Qualifications* Experience with federal or scientific data systems (NOAA, NASA, USGS, etc.)
- Exposure to Generative AI tools (e.g., OpenAI, Anthropic, Cohere) and prompt engineering techniques
- Understanding of federal data security standards (e.g., NIST 800-53, FISMA, FedRAMP)
- Experience with dashboarding and visualization tools (e.g., Tableau, Power BI, Dash)
What BLN24 brings to the Game:
BLN24 benefits are game changing. We like our team to play hard and that means they need to be taken care of — physically, financially, and emotionally. We make sure to keep them in the game by giving them access to generous medical, dental, and vision plans.* You can join one of the fastest growing companies headquartered in the Washington DC Metro Area. We give you the opportunity to work in different sectors, so you have the chance at variety while maintaining stability.
- Flexibility at BLN24 allows each individual the opportunity to balance quality work and their personal lives. Depending on projects, we allow remote working opportunities so you can always be in the game no matter where you call home.
BLN24 is an Equal Opportunity Employer. We believe people are our strength and understand diverse talents are key to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. In accordance with applicable law, we make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as any mental health or physical disability needs.
hVmrmAhF08
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At BLN24, 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 $212,040 based on 13,780 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 $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
BLN24 AI Hiring
BLN24 has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.