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Generative Ai Developer Jobs in Colorado (NOW HIRING)

asrcfh

AI/ML Software Engineer

Colorado Springs, CO · On-site

Experience with Large Language Models/Generative AI and Prompt Engineering. Also, using LLM/GAI solutions for usage in human-machine teaming for Software Development * Familiarity with Linux ...

asrcfh

AI/ML Software Engineer

Colorado Springs, CO · On-site

Experience with Large Language Models/Generative AI and Prompt Engineering. Also, using LLM/GAI solutions for usage in human-machine teaming for Software Development * Familiarity with Linux ...

Litera

Senior AI Software Engineer

Denver, CO

$126.10K - $166.20K/yr

The Opportunity The Generative AI Innovation Team is transforming how Litera-and its clients-leverage data for competitive advantage. As a Senior AI Engineer, you will play a critical role in shaping ...

asrcfh

AI/ML Software Engineer

Colorado Springs, CO · On-site

Experience with Large Language Models/Generative AI and Prompt Engineering. Also, using LLM/GAI solutions for usage in human-machine teaming for Software Development * Familiarity with Linux ...

Relativity

Senior Applied Scientist

Denver, CO · On-site +1

Develop machine learning and generative AI models that ship as customer-facing product features * Collaborate closely with engineers to write production-quality code and contribute across the full ...

Litera

Senior AI Software Engineer

Denver, CO · On-site

$126.10K - $166.20K/yr

The Opportunity The Generative AI Innovation Team is transforming how Litera-and its clients-leverage data for competitive advantage. As a Senior AI Engineer, you will play a critical role in shaping ...

Guidehouse

AI / Data Engineer

Fort Collins, CO · On-site

$113.30K - $136.10K/yr

Applied experience with machine learning, generative AI, large language models, retrieval-augmented generation, embeddings, vector databases, semantic search, or prompt engineering. * Experience ...

CableLabs

Lead AI/ML Engineer

Louisville, CO · On-site

$107.10K - $141.10K/yr

Experience with LLMs, generative AI, agentic workflows, retrieval-augmented generation (RAG), and ... Experience developing software reference implementations, APIs, and developer-facing platforms

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Generative Ai Developer information

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How much do generative ai developer jobs pay per hour?

As of May 29, 2026, the average hourly pay for generative ai developer in Colorado is $47.62, according to ZipRecruiter salary data. Most workers in this role earn between $24.76 and $57.64 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Generative AI Developer, and why are they important?

To thrive as a Generative AI Developer, you need strong programming skills (especially in Python), a deep understanding of machine learning concepts, and an advanced degree in computer science or a related field. Familiarity with frameworks like TensorFlow, PyTorch, and experience with cloud platforms or model deployment tools are typically required. Creative problem-solving, adaptability, and effective collaboration are standout soft skills in this evolving field. These abilities are crucial to design, implement, and refine generative models that solve real-world problems and drive innovation.

What are some common challenges faced by Generative AI Developers when deploying models in production environments?

Generative AI Developers often encounter challenges such as ensuring model reliability, managing computational resource requirements, and addressing ethical considerations like data bias or content safety. Deploying generative models at scale requires robust monitoring to detect unexpected outputs or model drift, and collaboration with data engineers and product teams to optimize performance. Staying up-to-date with evolving frameworks and best practices is essential, as production environments demand both technical rigor and adaptability to new AI advancements.

What is a Generative AI Developer?

A Generative AI Developer is a technology professional who specializes in designing, building, and deploying artificial intelligence systems that can create new content, such as text, images, audio, or code. They work with advanced machine learning models, like generative adversarial networks (GANs) or large language models, to enable computers to produce original outputs. These developers often collaborate with data scientists, researchers, and product teams to integrate AI-generated content into software applications and business solutions.

What is the difference between Generative Ai Developer vs Machine Learning Engineer?

AspectGenerative Ai DeveloperMachine Learning Engineer
CredentialsBachelor's or higher in CS, AI, or related fields; experience with deep learning frameworksBachelor's or higher in CS, Data Science, or related fields; strong programming skills
Work EnvironmentDevelops AI models for content creation, chatbots, and creative applicationsBuilds and deploys ML models for various data-driven solutions across industries
Industry UsageTech, entertainment, marketing, and creative sectorsFinance, healthcare, tech, and e-commerce sectors

While both roles involve AI and machine learning, Generative Ai Developers focus on creating models that generate content, such as images or text, whereas Machine Learning Engineers develop broader ML solutions for diverse applications. The roles often overlap but differ mainly in their specific focus areas and use cases.

What are popular job titles related to Generative Ai Developer jobs in Colorado? For Generative Ai Developer jobs in Colorado, the most frequently searched job titles are:
What job categories do people searching Generative Ai Developer jobs in Colorado look for? The top searched job categories for Generative Ai Developer jobs in Colorado are:
What cities in Colorado are hiring for Generative Ai Developer jobs? Cities in Colorado with the most Generative Ai Developer job openings:
Generative Ai Developer jobs near you
Infographic showing various Generative Ai Developer job openings in Colorado as of May 2026, with employment types broken down into 42% Full Time, 51% Part Time, and 7% Contract. Highlights an 83% Physical, 1% Hybrid, and 16% Remote job distribution, with an average salary of $99,052 per year, or $47.6 per hour.

Product Manager - Enverus AI - Multiple levels - 26131

Drilling Info

Denver, CO

Other

Medical, Dental, Vision, Life

Posted 15 days ago

Job description

Description
Why YOU want this position
At Enverus, we're committed to empowering the global quality of life by helping our customers make energy affordable and accessible to the world.
We are the most trusted energy-dedicated SaaS company, with a platform built to maximize value from generative AI, and our innovative solutions are reshaping the way energy is consumed and managed. By offering anytime, anywhere access to analytics and insights, we're helping our customers make better decisions that help provide communities around the world with clean, affordable energy.
 
The energy industry is changing fast. But we've continued to lead the way in energy technology, creating intelligent connections across the entire energy ecosystem, from renewables, power and utilities, to oil and gas and financial institutions. Our solutions create more efficient production and distribution, capital allocation, renewable energy development, investment and sourcing, and help reduce costs by automating crucial business operations. Of course, this wouldn't be possible without our people, which is why we have built a team of individuals from a diverse range of backgrounds.
 
Are you ready to help power the global quality of life? Join Enverus, and be a part of creating a brighter, more sustainable tomorrow.
 
The Role
We are hiring across multiple levels - Product Manager, Senior Product Manager, and Principal Product Manager - to join the Enverus AI team, working across the Enverus ONE platform, our governed AI execution layer for the energy industry.
Enverus ONE is where generative AI meets domain-specific energy workflows: agents that execute, flows that orchestrate, applications that drive decisions, and the data foundation that makes any of it possible. The pace is fast, the problems are real, and the customers are among the largest operators, investors, traders, utilities, and service companies in the global energy industry.
Because generative AI is evolving by the week, responsibilities within this team are intentionally fluid. Focus areas shift as the technology landscape, customer demands, and platform priorities evolve. The person in this role should be energized - not unsettled - by that reality. Rapid change is the operating environment here, not an exception to manage around.
This is a role for someone who is hands-on by nature, fast by habit, and technically serious about generative AI. The best candidate is already building with agents, already deploying things to real users, and already comfortable making calls in ambiguous, fast-moving environments. They are strategic and tactical in the same week: able to frame a multi-quarter direction, then break it into phases and work shoulder-to-shoulder with engineering and data science through execution and delivery.
We are hiring the person, not the title. This posting is open at the Product Manager, Senior Product Manager, and Principal Product Manager levels. The Enverus AI team's shape evolves with the technology - new surfaces emerge, scopes expand, and the work reshapes itself every quarter. Rather than force a specific level, we are matching scope to the right person. If you are strong, the role will flex to fit you. Tell us what you've shipped, and we will tell you where you land.
One expectation does not change with level: everyone on this team executes. Scope, strategic weight, and stakeholder reach grow as you get more senior. What stays constant is the requirement to roll up your sleeves, get into the weeds, and deliver alongside engineering, data science, and commercial. Principal PMs on this team do more strategy and still write stories, sit in standups, test agents, and ship. If "I'm too senior for that" is ever in your head, this isn't the team for you.
 
Performance Objectives
  • Ship agentic products end-to-end. Own generative AI products from ideation and prototype through production deployment, adoption, and iteration. Define agent behavior, tool use, evaluations, guardrails, and the user experience around autonomous and semi-agentic workflows.
  • Translate strategy into phased execution. Frame the long-horizon product direction, then break it into phases, milestones, and sprint-level work. Stay in the details with engineering and data science through delivery - not just at the planning stage. The handoff from strategy to execution is your job, not someone else's.
  • Work directly with energy customers. Sit in on deployments, run discovery, lead demos, shadow real users, and bring customer problems back into the product. Customer proximity is the primary input to product direction, not an afterthought.
  • Operate hands-on in the product. Use Enverus ONE daily. Test workflows yourself. Use coding agents (Claude Code, Cursor, or equivalents) in your own work. Prototype when needed. Ground your direction in firsthand experience, not secondhand reports.
  • Drive cross-functional execution. Coordinate across AI engineering, platform teams, data strategy, domain PMs, customer success, marketing, and sales. Surface blockers early, drive alignment without formal authority, and keep work moving across pods, time zones, and geographies.
  • Own backlog, sprints, and delivery. Maintain a clean, prioritized backlog. Write development-ready stories with clear acceptance criteria. Participate actively in agile ceremonies. Keep the team moving with urgency and clarity.
  • Shape commercialization and GTM. Partner with commercial leaders on packaging, pricing, access models, and launch readiness. Translate technical capability into customer-facing value and clear product narratives.
  • Communicate strategy and outcomes. Deliver structured, honest updates to leadership, product peers, and commercial teams. At the Principal level, this extends to executive-facing and external customer communication.
  • Adapt as priorities move. The roadmap will shift. The technology will shift. Re-plan fast, re-scope cleanly, and keep the team productive through the change.
Competitive Candidate Profile
We are open to hiring this role at the Product Manager, Senior Product Manager, or Principal Product Manager level. The expectations below apply to every candidate regardless of level; level-specific expectations follow.
What we expect from every candidate
  • Hands-on experience building agents. You have personally designed, built, or shipped generative AI agents - not just managed a team that did. You're fluent in tool use, retrieval patterns, orchestration, evaluation, and the real work of getting agentic systems to behave reliably in production.
  • Technical depth in generative AI. Strong working understanding of LLMs, prompting, fine-tuning, RAG, agent frameworks, model evaluation, and where the frontier is today. You can hold a serious conversation with AI engineers and data scientists without a translator.
  • Strategic and tactical in equal measure. You operate at the whiteboard and in the backlog on the same day. You formulate big-picture strategy, break it into phases, and work directly with technology and data science through execution and delivery.
  • Customer-facing instinct. Comfortable in the room with enterprise customers. You run discovery, lead demos, sit through deployments, and take hard feedback without defensiveness.
  • End-to-end delivery orientation. You own the full lifecycle - requirements, sprints, QA, launch, adoption, iteration - rather than a slice of it. Active product participant, not a pure requirements-writer.
  • Bias for speed. You ship fast in ambiguous environments. You don't wait for perfect conditions. Build, test, learn, iterate.
  • Can-do attitude. Your default mode is "I'll figure it out." You identify what needs doing and do it. You don't escalate when you should execute.
  • Comfort with rapid change. You thrive when the plan shifts. If you need stability to do your best work, this role will frustrate you. If you enjoy the adjustment, you'll do your best work here.
  • Cross-functional influence. You work effectively across teams, functions, and geographies without formal authority. You build relationships and maintain alignment through consistent, reliable follow-through.
  • Crisp communication. Structured writer and speaker who can translate frontier-stage work into compelling narratives for executives, customers, and engineers.
  • Energy industry experience (strongly preferred). Prior work with upstream, midstream, power and renewables, trading, financial institutions, or OFS is a significant advantage. Not a hard requirement, but candidates should be able to get up to speed fast and stay credible in front of energy customers.
What changes by level
 
Across every level, you are expected to be a hands-on practitioner who executes directly with the team. What changes is the scope, strategic reach, and stakeholder weight of the work - not whether you get into the weeds.
Product Manager (typically 3-5 years of PM experience)
  • Scope: A specific workstream or product surface.
  • Strategy: Contributes to strategy for your area; primarily accountable for execution quality and delivery velocity.
  • Execution: Drives day-to-day backlog, sprint execution, QA, and cross-functional coordination with engineering and data science.
  • Communication: Within the product and engineering organization.
Senior Product Manager (typically 5-8 years of PM experience)
  • Scope: A larger product area or multiple connected workstreams.
  • Strategy: Defines roadmap and phasing; shapes direction for your area.
  • Execution: Runs execution directly alongside engineering and data science - same backlog ownership, sprint participation, and hands-on product engagement as PM level, just across a wider surface.
  • Communication: Across product, engineering, and commercial leadership. Leads customer engagements and contributes to commercialization decisions.
Principal Product Manager (typically 8+ years of PM experience)
  • Scope: A significant slice of the Enverus AI portfolio; influences platform decisions that span multiple teams and pods.
  • Strategy: Sets strategic direction. Shapes commercialization, packaging, pricing, and GTM readiness with commercial leaders. Has taken internal or early-stage AI products to market before.
  • Execution: Still a hands-on practitioner. Writes stories when needed, sits in standups, tests agents, debugs flows, and ships alongside the team. The strategic work is additive to execution, not a replacement for it.
  • Communication: Executive-facing and external customer communication responsibility.
Enverus offers comprehensive benefits to our employees to include:
  • Medical
  • Dental
  • Vision
  • Income Protection (disability, life/AD&D, critical illness, accident)
  • Employee Assistance Program (EAP)
  • Healthcare Spending Account (HSA), Commuter
  • Lifestyle & Wellbeing Program
  • Pet Insurance

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