Applied AI Engineer

CyberCoders, Inc.; United States; On-site; USD 150,000 - 210,000; Full time

Job summary

CyberCoders, Inc. is seeking an Applied AI Engineer to own AI-driven product initiatives across multiple portfolio companies.

You will build end-to-end features, refactor legacy systems into AI-first architectures, and ship production-ready solutions while collaborating with CPOs and CTOs to maximize business impact. Role emphasizes hands-on coding, rapid prototyping, and delivering actionable AI-enabled improvements.

Qualifications

• Hands-on, end-to-end contributor capable of shipping features.
• Ability to identify AI opportunities and transform legacy systems.

Responsibilities

• Build full-stack: frontend, API, data layer, infrastructure; ship complete features.
• Refactor legacy to AI-first architectures and identify AI-enabled improvements.
• Prototype quickly with AI assistants, automation, models and smart features.
• Ship to production with testing, monitoring, and docs.
• Collaborate with portfolio engineers, pair-programming and knowledge transfer.
• Leave behind working software, trained teams, and playbooks.
• Analyze market dynamics and customer workflows to find AI disruption opportunities.
• Partner with CPOs/CTOs to align AI initiatives with business goals.
• Turn opportunities into concrete product roadmaps with milestones.
• Validate assumptions with customers and refine product direction.
• Evaluate existing architectures for AI-first transformation.

Skills

Full-stack development

Product engineering

AI-native architectures

Tools

Cursor

Claude Code

Job description

We are hiring an Applied AI Engineer (you could also be a Founder, Product Engineer, Founding Engineer, Forward Deployed engineer or Technical Product Leader). This is a hands-on technical role for people who think in code, ship in weeks, and measure success in business impact. You'll work across multiple portfolio companies simultaneously, partnering with CPOs and CTOs to identify AI-native product opportunities, refactor legacy systems into AI-first architectures, and build the next generation of their products. You'll operate with the autonomy of a startup founder while leveraging the resources and portfolio access of a leading private equity firm. We're looking for true full-stack builders and product engineers. People who can own a product end-to-end, from identifying the opportunity to shipping the solution. You're equally comfortable talking product strategy with a CPO, architecting systems with a CTO, writing production code with engineers, and identifying business processes and outcomes in partnership with the COO that are ripe for automation.

What you are doing:

Embed & Build (50%)

• You'll work across multiple portfolio companies in parallel, with engagements averaging six months. You're embedded as a builder with commit access: Build full-stack: Own the entire product surface, frontend, API, data layer, infrastructure. Ship complete features beyond just backend scripts
• Refactor legacy to AI-first: Transform existing products and workflows into AI-native architectures. Identify where AI can replace, augment, or reimagine current functionality.
• Prototype in days: Build working proof-of-concepts fast. AI assistants, workflow automation, predictive models, intelligent features, and whatever solves the highest-impact problem
• Ship to production: Take prototypes to production-grade systems with proper testing, monitoring, and documentation.
• Pair with engineers: Work alongside portfolio company developers, transferring knowledge through code review, pairing sessions, and building together.
• Leave systems, not slides: When engagements evolve, you leave behind working software, documented code, trained teams, and playbooks
• Identify AI disruption opportunities: Analyze market dynamics, customer workflows, and competitive landscape to find where AI can create step-change product advantages
• Partner with CPOs/CTOs: Work as a strategic thought partner to product and technical leadership, bringing an informed perspective on what's possible with AI
• Scope and prioritize: Turn opportunities into concrete product roadmaps with clear milestones, success metrics, and resource requirements
• Validate with customers: Get in front of customers to validate assumptions, understand pain points, and refine product direction
• Assess legacy systems: Evaluate existing product architectures and identify the highest-leverage paths to AI-first transformation

Discover & Evaluate Tools (15%)

• Scout the AI landscape: Continuously discover, evaluate, and test new AI tools, frameworks, and platforms as the ecosystem evolves
• Build the toolkit: Curate and maintain a best-in-class set of AI development tools, MCP servers, and reusable components for deployment across the portfolio
• Benchmark and recommend: Run structured evaluations of new tools against real portfolio use cases; make clear recommendations on what to adopt, what to skip
• Stay at the frontier: Be the person who knows what shipped last week and what's coming next month in AI tooling

Train & Enable Teams (15%)

• Train on AI best practices: Teach portfolio company teams how to build with AI. Prompt engineering, model selection, RAG architectures, agent design, evaluation methods
• Level up dev workflows: Train engineers on AI-native development using Cursor, Claude Code, and similar tools; transform how they write, review, and ship code
• Create learning resources: Build playbooks, workshops, and documentation that portfolio teams can use long after your engagement
• Productize patterns: Turn solutions into reusable templates and frameworks deployable across the portfolio
• Raise the bar: Leave every team more capable of building with AI than when you arrived

What you need:

• You ship complete products from frontend to infrastr