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About This Role
Cinteot Inc. is a small IT services company that specializes in cybersecurity, Big Data/databases, software development, systems testing, STIG Compliance training, closed\-circuit television/security cameras and access controls, and construction/facilities.
We are a Woman\-owned, SBA Certified 8(a), and HUBZone company.
Job Overview:
The AI Data Engineer is responsible for building and operating high‑quality, governed, and AI‑ready data pipelines that power enterprise GenAI and agent‑based use cases. This role focuses on preparing data for retrieval‑augmented generation (RAG), managing embeddings and vector indexes, and ensuring data quality, lineage, and compliance across the AI platform. As part of the AI CoE Technology pod, the AI Data Engineer enables rapid, responsible AI development by delivering reusable, scalable data foundations. This is a hands‑on individual contributor role within the AI CoE – Technology pod, working closely with AI Platform Engineers and AI Engineers to support both shared platform capabilities and priority AI use cases. The role is intentionally centralized to avoid fragmented data pipelines and to ensure consistent governance, quality, and reuse across the enterprise.
Major Responsibilities:
- Design, build, and maintain data pipelines that ingest, transform, and curate structured and unstructured data for AI use cases.
- Prepare RAG‑ready datasets by applying metadata enrichment, chunking, normalization, and document parsing patterns aligned to platform standards.
- Partner with source system teams and domain SMEs to understand data semantics and ensure accurate representation for AI consumption.
- Create and maintain embedding pipelines, including generation, refresh, and lifecycle management.
- Own vector index maintenance, including re‑indexing strategies, performance tuning, and cleanup of stale or unused embeddings.
- Support knowledge grounding for AI agents by ensuring source attribution, consistency, and traceability.
- Implement data quality checks, validation rules, and monitoring to ensure accuracy, completeness, and reliability of AI datasets.
- Ensure all AI data pipelines comply with enterprise data governance, privacy, and information management policies, including support for regulated and sensitive data use cases.
- Collaborate with Architecture, Security, and Information Governance partners to align data handling with approved AI patterns and risk controls.
- Support AI Engineers during onboarding and troubleshooting by diagnosing data issues that affect agent behavior or retrieval accuracy.
- Contribute reusable data patterns, templates, and documentation to accelerate future AI use cases.
- Participate in platform support activities defined in the AI CoE RACI, particularly those related to data grounding and vector maintenance.
- Optimize data and embedding pipelines for performance, scalability, and cost efficiency, in partnership with Platform Engineers.
- Monitor data freshness and usage trends to recommend retirement, refresh, or enhancement of datasets supporting AI agents.
Qualifications:
- Bachelor’s degree in computer science, Engineering, Data Science, or a related technical discipline OR equivalent combination of education and relevant experience.
- Demonstrated experience designing and operating production\-grade data pipelines in an enterprise environment.
- Experience working with unstructured data (documents, text, PDFs) and preparing data for analytics, ML, or AI use cases.
- Working knowledge of embeddings, vector databases, and retrieval patterns used in modern AI and GenAI solutions.
- Strong understanding of data quality, lineage, and governance concepts.
Additional Licensing, Certifications, Registrations:
- Professional certification(s) in area of expertise a plus
- AWS Machine Learning Specialty, Azure AI Engineer Associate, or equivalent cloud certifications.
- Databricks and/or Snowflake certifications
Knowledge, Skills, and Abilities:
- Strong hands\-on experience designing and operating data pipelines for analytics, ML, or AI workloads.
- Experience working with unstructured data (documents, PDFs, text) and preparing it for downstream AI or search use cases.
- Knowledge of embeddings, vector databases, and retrieval patterns used in RAG or knowledge\-based AI systems.
- Strong understanding of data quality, lineage, and governance concepts in enterprise environments.
- Experience supporting GenAI or agentic AI platforms in a regulated enterprise environment (e.g., healthcare, financial services).
- Familiarity with cloud\-native data services and AI platforms commonly used for enterprise AI enablement.
- Experience partnering with platform and application teams in a federated or hub\-and\-spoke operating model.
- Understanding of healthcare compliance standards (HIPAA, HITRUST) and ethical AI practices (bias, explainability,data privacy).
- Ability to collaborate effectively with cross\-functional teams and translate business requirements into technical solutions.
- Strong problem\-solving and innovation mindset, with the ability to adapt generative AI to real\-world challenges in healthcare and ability to adapt to and adapt to evolving priorities and technologies
- Familiarity with governance and compliance frameworks relevant to healthcare (HIPAA, SOC 2, HITRUST) preferred
Benefits:
- Complete Insurance Coverage
- Blue Cross Medical, Delta Dental, Vision, Life
- 401k with Company Contribution
- Tuition Reimbursement
- Generous Paid Time Off (including your birthday!)
Cinteot is an Equal Opportunity Employer
All qualified applicants will receive consideration for employment without regard to race, sex, color, religion, sexual orientation, gender identity, national origin, protected veteran status, or on the basis of disability.
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 3,708 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Cinteot, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $178,800 based on 40 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Cinteot AI Hiring
Cinteot has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer. Based in Newark, NJ, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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