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HomeCompaniesDdc Dine Careers Icims ComLow Code AI Engineer

Low Code AI Engineer

Ddc Dine Careers Icims Com · Remote, UNAVAILABLE, US · Remote · Active · iCIMS

Job facts

FieldValue
CompanyDdc Dine Careers Icims Com
TitleLow Code AI Engineer
Normalized title-
Department / teamNOVA-Diné
LocationUNAVAILABLE, United States
Work modelRemote / Remote
Employment typeOTHER
Salary-
Statusactive
ATS provideriCIMS
Posted / first seen2026-01-30 / 2026-05-31
Changed / last seen2026-06-01 / 2026-06-06

Related slices

PageWhat it containsOpen
Company jobsActive postings from Ddc Dine Careers Icims Com.Open
Company breakdownsRole, location, ATS, and work model facets for this company.Open
ATS provider jobsActive postings observed through iCIMS.Open
Provider filtered searchThe same provider as a filtered job collection.Open
Department jobsActive postings in NOVA-Diné.Open
Work model jobsActive Remote postings.Open
Lifecycle eventsOpen, update, close, and reopen events for this posting.Open
Original postingCanonical source or apply URL captured from the ATS.Open

Linked records

CompanyDdc Dine Careers Icims Com
Source97a23737-d232-4b76-87a3-abf6bc4d7563
ATS provideriCIMS

Description

Job Summary: The Low Code AI Engineer will support the International Trade Administration (ITA) AI Center of Excellence (AI‑CoE) by developing, deploying, and maintaining scalable artificial intelligence and machine learning solutions. This role focuses on building predictive models, automating ML pipelines, and operationalizing AI solutions using low‑code and cloud‑based platforms within a federal environment. *This position is contingent upon contract award.* Job Duties and Responsibilities: Develop algorithms and predictive models using machine learning and deep learning frameworks. Scale AI/ML prototypes into production‑ready solutions. Preprocess, validate, and manage structured and unstructured datasets. Automate, orchestrate, and monitor machine learning pipelines. Manage versioning, deployment, and lifecycle of models and datasets. Ensure data quality, accuracy, and integrity throughout the ML lifecycle. Serve and scale ML models in cloud environments. Deploy, monitor, and maintain AI/ML solutions in development, staging, and production. Collaborate with cross‑functional teams using SAFe Agile methodologies. Utilize Government‑provided DevOps and cloud platforms. Produce clear technical documentation compliant with federal standards. Other duties as assigned. Job Requirements (Education/Skills/Experience): U.S. Citizenship (required). MUST have a NACI or higher background investigation. Minimum two (2) years of experience. Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field (or equivalent experience). Experience developing and deploying machine learning models. Hands‑on experience with data preprocessing, feature engineering, and model evaluation. Familiarity with ML pipeline automation and orchestration tools. Experience working in Agile or SAFe environments. Desired Qualifications: Master’s degree in a related technical field. Experience with low‑code or no‑code AI/ML platforms. Experience deploying models in cloud environments (AWS, Azure, or GCP). Knowledge of MLOps practices, CI/CD pipelines, and monitoring tools. Prior experience supporting federal or government clients. Familiarity with AI governance, ethics, and security considerations. Experience using Azure DevOps or similar tools. Work Location: Remote work preferred with occasional on‑site support in Washington, DC, as required.This contractor and subcontractor shall abide by the requirements of 41 CFR 60-1.4(a), 60-300.5(a) and 60-741.5(a). These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity, national origin, or for inquiring about, discussing, or disclosing information about compensation, or any other basis prohibited by law. We participate in E-Verify.

Full job record

Job ID890df8517270d9c8aa6a879fc405ec9bda08dd28
Org ID2c9b7e45-880d-44b2-ac8e-948f46a4f890
Source ID97a23737-d232-4b76-87a3-abf6bc4d7563
Board ID97a23737-d232-4b76-87a3-abf6bc4d7563
Providericims
Provider Job Key5901
TitleLow Code AI Engineer
Normalized Title
Statusactive
Activeyes
Location TextRemote, UNAVAILABLE, US
DepartmentNOVA-Diné
Team
Employment TypeOTHER
Workplace Typeremote
Remote Policyremote
CountryUnited States
RegionUNAVAILABLE
City
Salary RawJob Summary: The Low Code AI Engineer will support the International Trade Administration (ITA) AI Center of Excellence (AI‑CoE) by developing, deploying, and maintaining scalable artificial intelligence and machine learning solutions. This role focuses on building predictive models, automating ML pipelines, and operationalizing AI solutions using low‑code and cloud‑based platforms within a federal environment. *This position is contingent upon contract award.* Job Duties and Responsibilities: Develop algorithms and predictive models using machine learning and deep learning frameworks. Scale AI/ML prototypes into production‑ready solutions. Preprocess, validate, and manage structured and unstructured datasets. Automate, orchestrate, and monitor machine learning pipelines. Manage versioning, deployment, and lifecycle of models and datasets. Ensure data quality, accuracy, and integrity throughout the ML lifecycle. Serve and scale ML models in cloud environments. Deploy, monitor, and maintain AI/ML solutions in development, staging, and production. Collaborate with cross‑functional teams using SAFe Agile methodologies. Utilize Government‑provided DevOps and cloud platforms. Produce clear technical documentation compliant with federal standards. Other duties as assigned. Job Requirements (Education/Skills/Experience): U.S. Citizenship (required). MUST have a NACI or higher background investigation. Minimum two (2) years of experience. Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field (or equivalent experience). Experience developing and deploying machine learning models. Hands‑on experience with data preprocessing, feature engineering, and model evaluation. Familiarity with ML pipeline automation and orchestration tools. Experience working in Agile or SAFe environments. Desired Qualifications: Master’s degree in a related technical field. Experience with low‑code or no‑code AI/ML platforms. Experience deploying models in cloud environments (AWS, Azure, or GCP). Knowledge of MLOps practices, CI/CD pipelines, and monitoring tools. Prior experience supporting federal or government clients. Familiarity with AI governance, ethics, and security considerations. Experience using Azure DevOps or similar tools. Work Location: Remote work preferred with occasional on‑site support in Washington, DC, as required.This contractor and subcontractor shall abide by the requirements of 41 CFR 60-1.4(a), 60-300.5(a) and 60-741.5(a). These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity, national origin, or for inquiring about, discussing, or disclosing information about compensation, or any other basis prohibited by law. We participate in E-Verify.
Salary Min
Salary Max
Salary Currency
Salary Period
Source URLhttps://ddc-dine-careers.icims.com/jobs/5901/low-code-ai-engineer/job
Apply URLhttps://ddc-dine-careers.icims.com/jobs/5901/low-code-ai-engineer/job
First Seen At2026-05-31 18:43:35Z
Last Seen At2026-06-06 08:30:34Z
Last Checked At2026-06-06 08:30:34Z
Last Changed At2026-06-01 13:47:29Z
Inactive At
Source Posted At2026-01-30 05:00:00Z
Source Updated At2026-03-27 15:23:24Z
Raw Payload Uris3://job-postings-prod-raw-590183727216/raw/provider=icims/board=ddc-dine-careers.icims.com/date=2026-06-06/2026-06-06T08-30-32-575Z-cf5036582a34fd3a943ba99f1714838f7317afcf463a6fddfceaa6ac56b7564a.json
Event Fields
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Parsed Structured
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Extensions
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