Interested in this AI/ML Engineer role at Enterprise Knowledge?
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About This Role
Enterprise Knowledge (EK) is hiring a full-time Full Stack AI Developer to join our growing Enterprise Delivery team. In this role, you will bridge the gap between robust software engineering and cutting-edge Generative AI, designing and deploying end-to-end solutions that transform how global organizations interact with their data.
As a Full Stack AI Developer, you will be part of a team working on innovative projects, ranging from intelligent search interfaces to complex agentic AI systems that automate intricate business workflows. We are seeking a proactive, independent thinker with a passion for building functional, user-centric AI applications.
As an EKer, you will join a fast-growing company that is committed to equity and inclusion, have the opportunity to work in a collaborative workplace, take advantage of our unique benefits, and help build our innovative culture. To read more about the impactful work we are doing and to see the latest thought leadership from EK, follow us on LinkedIn.
Responsibilities:* Full Stack Development: Design, implement, and maintain scalable backend microservices (Python) and intuitive frontend interfaces (React/Vue) for AI-driven applications
- AI Orchestration: Contribute to the design and implementation of agentic AI solutions, including orchestration patterns, tool use, and memory/retrieval integration
- RAG & NLP: Design and operate end-to-end Retrieval Augmented Generation (RAG) workflows, including retrieval pipeline architecture, embedding strategies, and response evaluation
- Data Integration: Build and optimize data pipelines for ingesting and enriching structured and unstructured content using SQL and Python
- Semantic Integration: Work with internal teams to integrate AI components with semantic layers and knowledge graph implementations
- API & Middleware: Develop middleware integration layers for decoupled systems, including content auto-tagging and seamless search experiences
- Client Collaboration: Work with data subject matter experts and business users to understand their domain and communicate technical insights to non-technical stakeholders
Requirements
As a federal contractor, Enterprise Knowledge will not sponsor a new applicant for employment authorization or offer any immigration related support for this position (i.e., H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1, and O-1, or any EADs or other forms of work authorization that require immigration support from an employer.
This hybrid role requires that you live within a comfortable, commutable distance of our Arlington, VA office and be willing to be on-site 3 days a week.
Required Skills and Experience:* Education: Bachelor’s degree in Computer Science, Data Science, Math, or a related technical field
- Experience: 2 - 5 years of professional software development experience, with a focus on both backend and frontend technologies
- AI: Proven experience building and optimizing RAG pipelines (embeddings, reranking, and evaluation) and applying Natural Language Processing (NLP) techniques to unstructured data
- Programming: Proficiency in Python (preferred) or Java for backend development, and experience with modern JavaScript frameworks (e.g., React, TypeScript)
- Data & Databases: Familiarity with data modeling, database architecture, and SQL
- Tooling: Experience with code versioning tools (Git) and delivering solutions using Agile delivery practices
- AI-Augmented Development: Proficiency in using Generative AI tools to accelerate the full Software Development Life Cycle (SDLC). This includes leveraging AI for generating Architecture Decision Records (ADRs), drafting code, creating comprehensive test cases, and producing product documentation
- Communication: Strong ability to facilitate meetings, provide briefings, and present work products directly to clients
Preferred Skills and Experience:* Agentic Frameworks: Hands-on experience implementing agentic AI workflows using frameworks such as LangChain, LangGraph, LlamaIndex, or BAML
- Graph Technologies: Experience with RDF graph databases, SPARQL, or Linked Data technologies
- DevOps & Cloud: Comfort working with containerized environments (Docker) and cloud platforms (AWS, Azure, or GCP) for deploying AI solutions
- AI Observability: Experience implementing monitoring for AI components, including latency, success rate, and retrieval quality
- Semantic Web: Exposure to ontology/taxonomy design or management tools (e.g., Progress Semaphore, PoolParty)
Salary Information: EK considers a broad range of factors in considering employee salary, including a candidate’s skills, experience, education, certifications, past successes, and qualifications. The salary range for this role is $95,000 to $115,000, with most candidates likely to fall in the lower half. This range does not guarantee a specific salary and may be adjusted based on the needs of the company and the candidate’s qualifications.
Data Privacy Notice: Enterprise Knowledge (EK) is committed to protecting your personal information. When you submit your application, we collect and process your personal data solely for recruitment and hiring purposes. We will not use your information for any other purpose without your explicit consent.
We may share your information with third parties only as necessary to evaluate your application or comply with legal requirements. If we need to use your data for a purpose beyond the original intent or disclose it to additional third parties, we will provide you with prior notice.
You have the right to access the personal information we hold about you and to request corrections, amendments, or deletion of any inaccurate data or data processed in violation of applicable privacy principles. Requests for such changes will be reviewed and accommodated unless the burden or expense of providing access would be disproportionate to the risks to your privacy, or where the rights of other individuals may be affected.
By submitting your application, you acknowledge that you have read and understood this notice. If you have any questions about how we handle your personal data or need to update your information, please contact us at [email protected].
Salary Context
This $95K-$115K range is in the lower quartile 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 Enterprise Knowledge, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($105K) sits 50% below the category median. Disclosed range: $95K to $115K.
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.
Enterprise Knowledge AI Hiring
Enterprise Knowledge has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Arlington, VA, US. Compensation range: $115K - $115K.
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
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