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Decision Engineer Jobs in Colorado (NOW HIRING)

AI Engineer

Denver, CO ยท On-site

$105K - $115K/yr

Author Architecture Decision Records for non-trivial decisions and shepherd them to acceptance. * Mentor and unblock early-career engineers; review their work and grow their scope over time. CORE ...

New

Vantor is forging the new frontier of spatial intelligence, helping decision makers and operators ... They are seeking a hands-on Staff-level Applied AI Engineer to build the next generation of ...

$175K - $220K/yr

GovCIO is currently hiring for AI/ML Engineer develops and integrates advanced machine learning and large language model systems to support defense simulation, data fusion, and autonomous decision ...

Vantor is forging the new frontier of spatial intelligence, helping decision makers and operators ... You will operate as a roving specialist across our engineering organization, working at the ...

Chief Engineer This Project Lead/Chief Engineer serves as the senior technical leader and project ... Establish and maintain effective program governance, communication, decision-making, and execution ...

Chief Engineer This Project Lead/Chief Engineer serves as the senior technical leader and project ... Establish and maintain effective program governance, communication, decision-making, and execution ...

The Data Engineer at Hercules Industries is responsible for building, governing, and continuously improving the data foundation that powers decision-making across supply chain, operations, and ...

Data Engineer

Denver, CO ยท On-site

$90K - $107K/yr

The Data Engineer at Hercules Industries is responsible for building, governing, and continuously improving the data foundation that powers decision-making across supply chain, operations, and ...

The Data Engineer at Hercules Industries is responsible for building, governing, and continuously improving the data foundation that powers decision-making across supply chain, operations, and ...

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Decision Engineer information

What are the key skills and qualifications needed to thrive as a Decision Engineer, and why are they important?

To thrive as a Decision Engineer, you need strong analytical skills, expertise in data modeling, and a background in fields like operations research, mathematics, or computer science. Familiarity with decision analysis tools, optimization software (such as CPLEX or Gurobi), and programming languages like Python or R is typically required. Exceptional problem-solving abilities, communication skills, and the capacity to synthesize complex information are valuable soft skills in this role. These competencies enable Decision Engineers to develop effective solutions for complex business challenges and drive data-informed decision-making.

How does a Decision Engineer typically collaborate with data scientists and business stakeholders to deliver impactful solutions?

Decision Engineers frequently act as a bridge between technical teams and business stakeholders. In a typical workflow, they collaborate with data scientists to understand the underlying data models and analytical outputs, then work closely with business leaders to translate these insights into actionable strategies. This often involves facilitating discussions to clarify business objectives, ensuring analytical approaches align with end goals, and iteratively refining solutions based on feedback. Strong communication and project management skills are essential, as Decision Engineers must synthesize complex information and drive consensus among diverse teams.

What are Decision Engineers?

Decision Engineers are professionals who apply analytical, mathematical, and computational techniques to help organizations make data-driven decisions. They often use tools from operations research, data science, and systems engineering to evaluate complex options, optimize processes, and predict outcomes. Their work enables businesses to solve challenging problems, improve efficiency, and minimize risks in decision-making processes.

What is the difference between Decision Engineer vs Data Scientist?

AspectDecision EngineerData Scientist
Required credentialsBachelor's or master's in engineering, analytics, or related fieldsBachelor's or master's in statistics, computer science, or related fields
Work environmentFocus on designing decision models, algorithms, and optimization processesFocus on analyzing data, building predictive models, and extracting insights
Employer and industry usageUsed in industries like manufacturing, finance, and logistics for decision automationCommon in tech, finance, healthcare for data analysis and modeling

Decision Engineers primarily develop decision models and optimize processes to improve business outcomes, while Data Scientists analyze data to generate insights and predictive models. Both roles require strong analytical skills, but Decision Engineers focus more on decision automation and operational efficiency, whereas Data Scientists focus on data analysis and modeling.

What job categories do people searching Decision Engineer jobs in Colorado look for? The top searched job categories for Decision Engineer jobs in Colorado are:
What cities in Colorado are hiring for Decision Engineer jobs? Cities in Colorado with the most Decision Engineer job openings:

AI Engineer

BlueAngle LLC

Denver, CO โ€ข On-site

$105K - $115K/yr

Full-time, Contractor

Medical, Dental, Vision, Retirement, PTO

Posted 2 days ago

New


Job description

ABOUT THE ROLE

BlueAngle operates an Azure-based agentic operations platform and an internal Service Intelligence Platform that packages the firm's reusable capability modules. We are hiring an AI Engineer to own the secure infrastructure and delivery backbone beneath that platform, infrastructure-as-code, CI/CD, identity, and release automation. and to provide day-to-day technical mentorship to early-career engineers.

The role reports to the Platform & Technology Lead and works alongside the agentic engineering team. It exists to remove a single-point-of-bottleneck on the platform's secure, production-touching work and to let the team scale delivery without compromising governance.

SCOPE OF WORK

In scope. Azure infrastructure-as-code (Terraform) and workload provisioning; CI/CD pipeline engineering; identity and secrets (OIDC workload-identity federation, secret-store patterns, "zero long-lived keys"); the build-time pipeline that gates, packages, versions, and publishes the platform's reusable capability modules; integration server development; platform conventions, observability, and Architecture Decision Records.

Out of scope. Product roadmap and work-tracking ownership (held by the Lead); client-facing advisory delivery; authorship of capability-module content (enabled, not owned, by this role).

KEY RESPONSIBILITIES

  • Own and extend the IaC and CI/CD foundation across platform repositories to a consistent house standard - resource naming, tagging, identity federation, serverless stack, secret management, observability, and branch protection.
  • Provision new workloads end-to-end and bring them to a first secure deployment.
  • Author and review Terraform, pipeline workflows, and service code; uphold pull-request discipline and protected branch policy.
  • Design and operate the secrets-bearing and production-touching parts of the platform safely - federated credentials, release publishing, and key handling.
  • Build integration servers (including Model Context Protocol servers) and register them with the platform gateway.
  • Register platform components in the architecture catalog and keep specification, tracker, and repository in sync as the definition of done.
  • Author Architecture Decision Records for non-trivial decisions and shepherd them to acceptance.
  • Mentor and unblock early-career engineers; review their work and grow their scope over time.

CORE DELIVERABLES

Within the first engagement period, the role is expected to own and ship:

  • The Azure foundation and CI/CD for the capability-module publishing pipeline โ€” provisioning, identity federation, and release automation.
  • Reusable build, review-gate, and publish workflows adopted by the platform's owning repositories.
  • A scheduled reconciliation service that converges the published capability set into the managed workspace, with secure credential handling and a documented rotation policy.
  • A steady cadence of reviewed integration servers delivered to the gateway.

REQUIRED QUALIFICATIONS

Essential

  • Strong Azure: identity (Entra app registrations), serverless compute, key/secret stores, storage; OIDC / workload identity federation.
  • Terraform (modules, remote state, multi-environment) and CI/CD pipeline engineering (reusable and composite workflows).
  • Proficiency in Python and TypeScript; comfortable with shell/PowerShell for operational sequences.
  • A security-first IaC mindset (least privilege, no long-lived secrets, auditable releases) and disciplined version-control practice.
  • Clear technical writing (decision records, runbooks) and the temperament to mentor.

Desirable

  • Experience with agentic platforms or LLM tooling; familiarity with the Model Context Protocol and the Anthropic Claude tooling ecosystem.
  • API management, data-platform, and application-observability exposure.
  • Managed-services or M&A-IT context.

WAYS OF WORKING & GOVERNANCE

The definition of done is a synchronized specification, work-tracker, and repository, plus passing quality gates and green CI. Non-trivial decisions are captured as accepted Architecture Decision Records before build. The role participates in a platform unblock/on call rotation as it forms.

ENGAGEMENT

  • Type: Full-time, ongoing (a 3โ€“6 month contract-to-hire is also acceptable).
  • Location: Remote (US), hybrid optional.
  • Hours: Standard business hours.

SUCCESS CRITERIA - FIRST 90 DAYS

  • The capability-publishing pipeline's infrastructure and publish workflow are shipped and adopted by at least one owning repository.
  • The scheduled reconciliation service runs with secure credential handling and a documented rotation policy.
  • Early-career engineers' registration and packaging work reaches production without senior staff acting as a manual gate.
  • All delivered work passes the definition-of-done; key decisions are captured as accepted decision records.

Benefits:

  • 401(k)
  • Dental insurance
  • Health insurance
  • Paid time off
  • Professional development assistance
  • Vision insurance

Experience:

  • AI Models: 3 years (Required)
  • Azure: 3 years (Required)
  • Python & TypeScript: 2 years (Required)

Work Location: Remote Colorado