Interested in this AI/ML Engineer role at Nyla Technology Solutions?
Apply Now →Skills & Technologies
About This Role
Job Description
ACTIVE SECURITY CLEARANCE AT THE TS/SCI POLYGRAPH LEVEL IS REQUIRED
We are seeking an experienced Lead AI / NLP Engineer specializing in agentic AI frameworks and automated knowledge extraction to join our team supporting critical defense and intelligence customer missions. In this role, you will train and deploy advanced Natural Language Processing (NLP) and Named Entity Recognition (NER) models for Large Language Model (LLM) solutions using LangGraph. You will lead both supervised and unsupervised model training and validation workflows to perform dynamic knowledge extraction across multilingual, unstructured data streams without a predefined ontology. Additionally, you will work closely with customer data sources, implement robust data quality techniques, and navigate enterprise compliance and policy requirements to ensure end-user readiness and accessibility.
The annual base salary range for this role is $156,000-$184,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.
,
Required Skills
Agentic AI & LangGraph: Hands-on proficiency developing, orchestrating, and training NLP/NER capabilities for LLM solutions within agentic AI frameworks (specifically LangGraph).
NLP & Knowledge Extraction: Strong experience in both supervised and unsupervised model training, fine-tuning, and validation for automated knowledge extraction from unstructured, multi-language natural language data without relying on a predefined ontology.
Data Retrieval & Preprocessing: Deep understanding of customer data sources, retrieval techniques, and data preprocessing pipelines required to generate high-quality training and evaluation datasets.
Data Quality & Pipeline Readiness: Mastery of techniques to validate, clean, and enrich training data to ensure high data quality and seamless system integration.
Enterprise Compliance & Governance: Strong understanding of enterprise data compliance, security policies, and governance protocols to ensure AI solutions meet regulatory standards and end-user access controls.
Education: Bachelor’s Degree in Data Science, Computer Science, Computational Linguistics, Mathematics, or a related technical discipline, PLUS 10+ years of professional experience in data science, NLP, or software engineering OR Associate's Degree PLUS 12+ years of specialized technical experience in lieu of a degree.
,
Desired Skills
Multilingual NLP: Experience with cross-lingual embeddings, multilingual transformer architectures, and low-resource language processing.
Vector Databases & RAG: Familiarity with Retrieval-Augmented Generation (RAG) architectures, vector stores (e.g., Qdrant, ChromaDB, PGVector, Pinecone), and semantic search indexing.
Modern AI Tech Stack: Proficiency in Python and modern ML/DL libraries (PyTorch, Hugging Face Transformers, SpaCy, LangChain).
MLOps & Deployment: Experience deploying containerized AI microservices (Docker, Kubernetes) and setting up MLOps pipelines for continuous model monitoring and evaluation.
,
About Nyla Technology Solutions
Nyla Technology Solutions delivers exceptional Artificial Intelligence (AI), Data Science, and Software Engineering services for the U.S. Government. Nyla embraces a forward-thinking and bold approach at every turn, earning us a solid reputation of technical trendsetters within the industry. We have a passion for developing solutions that have a quick and immediate impact on mission. Headquartered in Columbia, Maryland, our customers love how we tackle their most challenging problems and get things done.
If you have the unique experience and expertise we are seeking, along with the desire and determination to invest your time and energy as a part of Nyla’s team,
Taking Care of All of You
Nyla provides a top-of-market compensation and benefits package. And through our unique Nyla FLEX program, we custom tailor these benefits to best fit your lifestyle.
The Nyla FLEX benefit program is designed to offer you flexibility in the 3 biggest areas of your life: your pay, your leave, and your schedule.
PAY - Nyla starts with 4 weeks of Annual Leave plus 11 holidays and an additional day of Annual Leave for each year you’re at the company. You have the flexibility to cash out your annual leave hours, opt out of other Nyla benefits, and/or arrange for additional hours on contract* (over 40 hrs/week). There’s even an option to earn 1.3 times your hourly rate once you work over 1880 hours on contract!
LEAVE - Want to spend more time with the family? Want more time to travel the world? You can BUY additional annual leave for a total of 6 weeks of annual leave. That’s up to 240 hours of leave plus 11 holidays! That’s not even including paid anniversary leave!
SCHEDULE - Does the traditional 40-hour workweek no longer fit your lifestyle? With Nyla FLEX, you have the freedom to scale down to 30-32 hours while still enjoying the top-notch Nyla benefits you know and love. It's flexibility that works for you without compromising the perks!
WHAT ABOUT OTHER BENEFITS? Nyla’s health care (medical, dental, and vision) is 100% covered by the company. We provide 10% 401k matching - with full vesting day 1! Our Professional Development offers $5,000 per year to be used towards fees, tuition, or time off for your continued growth. We even have a student loan repayment program and we provide 8 hours of volunteering annually so you can support your community, making your world a better place.
To learn more about Nyla's culture and our exceptional benefit packages click here.
Nyla is an equal opportunity employer.
Salary Context
This $156K-$184K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Nyla Technology Solutions, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($170K) sits 20% below the category median. Disclosed range: $156K to $184K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Nyla Technology Solutions AI Hiring
Nyla Technology Solutions has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Annapolis Junction, MD, US. Compensation range: $184K - $221K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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.