AI Collaborator/Developer

New Era Technology•Cincinnati, OH

6d•Hybrid

About The Position

Join New Era Technology, where People First is at the heart of everything we do. With a global team of over 3,000 professionals, we're committed to creating a workplace where everyone feels valued, empowered, and inspired to grow. Our mission is to securely connect people, places, and information with end-to-end technology solutions at scale. At New Era, you'll join a team-oriented culture that prioritizes your personal and professional development. Work alongside industry-certified experts, access continuous training, and enjoy competitive benefits. Guided by our core attributes — putting people first, embracing continuous learning, and thriving through collaboration and inclusion — we nurture our people to deliver exceptional customer service. If you want to make an impact in a supportive, growth-oriented environment, New Era is the place for you. Apply today and help us shape the future of work—together SUMMARY: Bring in an AI-first practitioner who compresses delivery timelines by orchestrating Claude (and other LLMs) across the full development lifecycle, while maintaining the architectural and product judgment that makes the output durable. This person is force-multiplier-shaped: one of them replaces a small team for the right kind of work and produces artifacts that survive enterprise review.

Requirements

Hands-on developer history combined with architectural and product ownership; can drop into code to debug, spike, or validate AI output, but doesn't default to hand-coding as the primary delivery mode
Experience owning product state and requirements, not just implementing tickets
Comfortable questioning legacy patterns, improving designs rather than replicating them, and pushing back constructively on stakeholder requests that don't hold up
Has worked inside enterprise environments with real constraints (security, change management, internal source control, data governance) — this is not a greenfield startup role
Uses LLMs to perform the majority of development and maintenance work; spends their own time on planning, orchestration, validation, and outcome alignment
Knows how to be prescriptive with AI — preventing it from doing the bare minimum, catching when it confabulates, and structuring prompts and context so output is fit-for-purpose the first time rather than the third
Exercises judgment about when not to use AI — recognizes when a 10-minute hand edit beats a 40-minute prompt-and-validate cycle. Avoids AI ceremony for its own sake.
Familiar with patterns like CLAUDE.md / project context files, MCP servers and tool integration, structured outputs, and managing parallel or long-running agent sessions
GitHub (comfortable with enterprise/internal GitHub instances, not just public repos); strong source control discipline
Claude Certified Architect (CCA) credential is a plus but not required; demonstrated production work matters more than the badge
Comfort with mainstream stack tools is assumed (CI/CD, basic cloud literacy, common IDEs)
Overall experience: 8–15+ years
AI-first / LLM workflow experience: 1–3+ years (or equivalent demonstrated production use)
English language skills

Nice To Haves

Claude (Desktop, Code, or both); experience configuring MCP servers and skills for enterprise contexts
SQL Server / data warehousing background
XML/XQuery exposure or willingness to ramp on MarkLogic
Insurance or other regulated-industry experience

Responsibilities

Directs and guides AI across planning, solution design, development, documentation, and UAT — treating the AI as a capable but literal collaborator that needs clear acceptance criteria to perform well
Defines what "good" looks like up front (acceptance criteria, conformance checks, architectural fit) and validates AI output against that standard rather than rubber-stamping it
Owns end-to-end delivery from ideation through production and iteration; acts as product thinker, architect, and delivery lead — not just an executor
Practices context engineering knows what reference material (code collections, schemas, utility libraries, prior decisions, SOPs) to feed the model, how to scope work so AI has enough context to succeed but not so much it drifts, and how to manage long-running sessions and context windows
Builds living documentation as a deliverable, not an afterthought — Mermaid/BPMN diagrams, structured SOPs, and conformance criteria that make AI output checkable against intent over time
Breaks work into well-defined, digestible units with clear acceptance criteria
Validates requirements with business stakeholders to surface and eliminate legacy holdovers rather than encoding them into new systems