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About This Role
Overview
Certified Technical Experts, Inc. (CTE) is seeking an experienced AI Engineer / Search Engineer to support the Federal Labor Relations Authority’s Intelligent Document Processing and AI-Enabled Search initiative. The project will modernize FLRA’s Microsoft SharePoint repository containing PDF documents into a structured, searchable knowledge repository supporting OCR, document classification, metadata extraction, relationship mapping, semantic search, vector search, natural-language queries, and source-grounded responses. The AI Engineer / Search Engineer will be responsible for designing, implementing, tuning, testing, and supporting the AI and enterprise search components of the solution. The role will focus on Azure AI Search, Azure OpenAI, Azure AI Document Intelligence, semantic and vector search, embeddings, Retrieval-Augmented Generation (RAG), metadata-driven retrieval, document classification, and source-grounded question answering within FLRA’s approved Microsoft 365 GCC/Azure environment.
Key Responsibilities
- Design and implement AI-enabled search capabilities for FLRA’s bargaining unit records.
- Configure and optimize Azure AI Search indexes, fields, filters, analyzers, semantic configurations, and vector search.
- Implement keyword, Boolean, full-text, metadata, semantic, and vector-based search capabilities.
- Develop and support natural-language question answering and source-grounded responses using Azure OpenAI or other approved AI services.
- Implement Retrieval-Augmented Generation (RAG) patterns using FLRA documents, extracted metadata, and search indexes.
- Generate, store, and manage embeddings used for semantic and vector retrieval.
- Ensure AI-generated responses cite or reference the originating Government documents.
- Support document classification, metadata extraction, and confidence scoring using Azure AI Document Intelligence and related AI capabilities.
- Develop logic for identifying and maintaining relationships among bargaining unit records, including amendments, consolidations, reorganizations, revocations, successorship determinations, and related cases.
- Develop and tune relevance-ranking strategies to improve search accuracy and usability.
- Configure metadata filters and structured retrieval using FLRA-required fields such as agency, bargaining unit, labor organization, case number, docket number, certification type, dates, region, and location.
- Support human-in-the-loop validation workflows for reviewing AI-generated classifications and metadata.
- Develop evaluation methods for search quality, retrieval relevance, classification accuracy, metadata extraction, and AI response quality.
- Tune prompts, retrieval strategies, chunking approaches, embeddings, and search configurations based on testing and Government feedback.
- Implement guardrails to ensure AI responses remain grounded in authorized FLRA content and do not generate unsupported information.
- Support automated ingestion and indexing of newly received documents.
- Work closely with the Azure Platform & Integration Engineer to integrate AI/search services with SharePoint, APIs, Azure services, and application components.
- Participate in system integration testing, UAT, defect resolution, production deployment, monitoring, and performance tuning.
- Maintain technical documentation covering AI models, prompts, search configurations, index schemas, evaluation methods, and operational procedures.
- Follow Federal security requirements, including CUI protection, FedRAMP-authorized services, least-privilege access, and NIST SP 800-53 controls.
Required Skills
- Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Technology, or related field, or equivalent professional experience.
- 5+ years of software, search, AI, machine learning, or data engineering experience.
- 3+ years of hands-on experience with enterprise search, AI/ML, or cloud-based AI solutions.
- Strong hands-on experience with Azure AI Search or comparable enterprise search technology.
- Experience with semantic search, vector search, embeddings, similarity search, and relevance ranking.
- Experience implementing Retrieval-Augmented Generation (RAG) solutions.
- Experience with Azure OpenAI, OpenAI APIs, or comparable large-language-model platforms.
- Experience developing source-grounded question-answering solutions.
- Strong experience with Python, C#/.NET, or similar programming languages.
- Experience with Azure AI Document Intelligence or comparable OCR/document-processing technology.
- Experience with document classification and structured metadata extraction.
- Experience working with unstructured content such as PDFs, scanned documents, and OCR-generated text.
- Experience designing search index schemas, metadata models, filters, and query strategies.
- Experience with text chunking, embedding generation, vector indexing, and retrieval strategies.
- Experience with prompt engineering and LLM integration.
- Experience evaluating and tuning search relevance and AI-generated responses.
- Understanding of hallucination mitigation, grounding, citations, confidence scoring, and responsible AI practices.
- Experience integrating AI/search services with REST APIs, Azure services, or enterprise applications.
- Familiarity with Microsoft SharePoint, Microsoft Graph API, and Microsoft 365 environments.
- Experience with Git-based source control, CI/CD, testing, and deployment practices.
- Understanding of secure cloud development, identity management, and least-privilege access.
- Strong analytical, troubleshooting, communication, and technical documentation skills.
- Ability to work closely with team and Government stakeholders.
- Ability to successfully complete required FLRA personnel security vetting.
Preferred Certifications
- Microsoft Certified: Azure AI Engineer Associate
- Microsoft Certified: Azure Data Scientist Associate
- Microsoft Certified: Azure Developer Associate
- Microsoft Certified: Azure Solutions Architect Expert
- Microsoft Certified: Azure Data Engineer Associate
- Microsoft Applied Skills related to Azure AI Search, Azure OpenAI, or generative AI
- CompTIA Security+ or equivalent security certification
Pay: $96,100.00 - $100,000.00 per year
Work Location: In person
Salary Context
This $96K-$100K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Certified Technical Experts, Inc., 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. This role's midpoint ($98K) sits 54% below the category median. Disclosed range: $96K to $100K.
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.
Certified Technical Experts, Inc. AI Hiring
Certified Technical Experts, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Peachtree Corners, GA, US. Compensation range: $100K - $100K.
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
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