AI Data Engineer

Hey — Let's find your next opportunity 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. About the job AI Data Engineer 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 Health Insurance Vision Insurance 401(k) Matching Paid Time Off (PTO) Paid Holidays Life Insurance Birthday PTO Education Reimbursement Preferred Skills Complete Insurance Coverage 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. Cinteot Reston, Virginia, United States 14 days ago Cribl Engineer Job Summary: We are seeking a highly experienced Cribl Engineer to serve as the principal technical authority for observability pipelines built on Cribl Stream and Cribl Edge. This role is designed for a senior technologist with deep expertise in log/telemetry routing, large‑scale data engineering, and enterprise‑grade observability architectures. You will shape pipeline strategy, design complex routing and transformation logic, drive platform reliability, mentor senior engineers, and serve as the top technical escalation point for Cribl‑related challenges. Key Responsibilities: Lead architecture and design for Cribl Stream/Edge across multiple enclaves and data domains. Build high throughput pipelines (multiTB/day) with advanced routing, filtering, enrichment, and replay workflows. Optimize system performance, worker topology, CPU/memory distribution, queues, and transport mechanisms. Engineer secure data flows with masking, tokenization, RBAC, PKI/TLS, and other governance controls. Integrate pipelines with SIEM/analytics ecosystems (Splunk, Elastic, SaaS telemetry platforms, cloud services). Develop HA/DR patterns, reliability frameworks, fleet health metrics, and failure mode response processes. Maintain reusable Cribl packs, shared patterns, runbooks, and operational standards. Serve as the senior escalation point for Cribl issues; interface with vendor engineering as required. Mentor engineers, conduct design reviews, drive engineering excellence, and enforce architectural standards. Support cross functional teams (security, cloud, analytics, infrastructure) on logging and telemetry strategy. Required Qualifications: 10+ years of experience in logging, observability, or SIEM engineering. 5+ years architecting enterprise scale log/telemetry pipelines. 3+ years hands‑on with Cribl Stream and Cribl Edge in production environments. Demonstrated success operating and scaling pipelines at 5–10+ TB/day. Expert‑level experience with Splunk forwarding/ingestion, source type management, and indexing practices. Strong understanding of transport protocols ( TCP, TLS/MTLS), Kafka, S3/object storage. Experience designing secure data flows, including encryption, RBAC, secrets management, and compliance controls. Demonstrated ability to mentor senior engineers and lead technical decision making. Certified Cribl Certified Engineer (CCOE) or equivalent Cribl product expertise. Must possess the following DoD 8570.01‑M certifications or be willing to obtain within 30 days of hire: Information Assurance Technician (IAT) Level II certification (currently Security+ CE, CCNA‑Security, GSEC, SSCP, CySA+, GICSP, or CND). IAT Level III certification requirements (currently CASP+ CE, CCNP Security, CISA, CISSP (or Associate), GCED, or GCIH). Cyber Security Service Provider (CSSP) - Infrastructure Support (IS) certification requirements (currently CEH, CySA+, GICSP, SSCP, CHFI, CFR, Cloud+, or CND). Must possess a TS/SCI with Polygraph Preferred Qualifications: Expertise creating and maintaining Cribl Packs and reusable pipelines. Experience with cloud telemetry (AWS, Azure, hybrid) and cross‑domain data movement patterns. Familiarity with NIST / CIS control frameworks and secure engineering practices. Experience building observability frameworks for large distributed systems. Competitive salary and performance‑based bonuses. Comprehensive health, dental, and vision insurance. Opportunities for professional development and continuous learning. At Cinteot, we foster a collaborative and inclusive work environment where every team member is valued. We prioritize innovation and continuous improvement, encouraging our employees to share ideas and drive change. Join us to be part of a culture that promotes growth, diversity, and excellence. Enterprise Security Architect Cinteot is seeking an experienced Enterprise Security Architect to join our Information Technology department. This remote position is ideal for individuals who are passionate about defining security architecture standards and creating frameworks that enable secure‑by‑design solutions. You will play a critical role in shaping our security landscape and driving the adoption of zero‑trust architecture across the enterprise. Key Responsibilities: Define enterprise security architecture standards and create security patterns. Establish security controls by application tier and document encryption standards. Lead the enterprise adoption of zero‑trust architecture through actionable templates. Review solution architectures for compliance with security patterns. Conduct threat modeling and establish security compliance frameworks. Required Qualifications: Bachelor's degree in Computer Science, Information Security, or related field. 7+ years in security architecture or cybersecurity. Proven experience with zero‑trust architecture and security controls frameworks. CISSP certification is required. Comprehensive health, dental, and vision insurance. Professional development opportunities and certifications support. At Cinteot, we foster a culture of collaboration, innovation, and continuous improvement. Our team values a pattern‑first mindset, enabling consistency and quality in our solutions. We are committed to supporting each other and working as strategic partners across all operational teams. AI Product Owner The AI Product Owner is responsible for defining, prioritizing, and delivering the enterprise AI platform vision, roadmap, and capabilities that enable Agentic AI, machine learning, and generative AI solutions across the organization. This role serves as the bridge between business stakeholders, AI architects, platform engineers, data engineers, data scientists, and governance teams to ensure the AI platform delivers measurable business value, scalability, security, and operational excellence. Key Responsibilities: Own and prioritize the Agile Team backlog for AI platform initiatives. Create and refine user stories, acceptance criteria, and feature decompositions. Define and maintain the product vision, strategy, and roadmap for enterprise AI platform capabilities. Translate business needs into clear user stories, acceptance criteria, and product requirements. Serve as the primary liaison between business stakeholders and AI platform delivery teams. Ensure platform capabilities align with organizational security, compliance, and AI governance requirements. Drive automation, standardization, and self‑service capabilities to improve developer productivity. Required Qualifications: High School Diploma/GED required. Bachelor’s degree in computer science, Engineering, Information Systems, Business, Data Science, or related field strongly preferred. 7+ years of experience in product management, technology delivery, platform management, AI/ML programs, or related disciplines. 3+ years of experience supporting AI, machine learning, data platforms, analytics platforms, or cloud‑based technology products. Experience working in Agile/Scrum product delivery environments. Strong understanding of AI/ML lifecycles, cloud platforms, and enterprise technology ecosystems. Flexible remote work arrangements to support work‑life balance. Comprehensive health and wellness benefits. Opportunities for professional development and career growth. At Cinteot, we believe in fostering an inclusive environment where diverse perspectives are valued. We are committed to innovation, collaboration, and continuous improvement, empowering our employees to thrive in our mission of delivering cutting‑edge AI solutions. #J-18808-Ljbffr SCALIS