Mid-Level AI Platform Engineer — SAIQ

Build the Future with AspenView Technology Partners

At AspenView, we are passionate about transforming the way organizations approach technology. We specialize in creating high-performing, nearshore IT teams to help North American clients innovate faster and more efficiently. As we continue to grow, we’re looking for exceptional people to join our team and help drive impactful change across industries.

Why Join AspenView?

At AspenView, we’re more than a nearshore IT partner—we’re a people-first, purpose-driven company that believes great culture drives great outcomes. We’re passionate about connecting talent and technology to deliver measurable value for clients—and meaningful career paths for our people.

Here’s what you can expect:

• Competitive base
• Flexible work model: hybrid, remote, or in-office
• Real growth opportunities and leadership visibility
• Inclusive, respectful culture that blends U.S. innovation with Colombian heart
• A company that listens, invests in you, and celebrates wins together

About the Role

We're looking for a Mid-Level AI Platform Engineer to support one of our strategic clients — a fast-moving, AI-native SaaS platform serving enterprise sales organizations. The platform runs multi-agent research and qualification workflows against public and proprietary data, delivering cited, auditable account briefs, stakeholder maps, and recommended actions directly inside the seller's CRM. It is multi-tenant, exposes an MCP server for external AI assistants to query, and is in live production use with enterprise customers.

This is a contract engagement (ongoing, with potential to extend or convert), fully remote within the United States, requiring overlap with US Eastern business hours. You'll report to the client's CTO & Co-Founder and work closely with two engineers, the Head of Product/Delivery, and the CSM team.

This person sits between engineering leadership and the development team, owning the operational half of engineering — production issues, customer-reported bugs, incremental product enhancements, release hygiene, and QA discipline — so leadership can stay focused on long-term architecture and roadmap rather than day-to-day firefighting.

What you will do:

Production & Output Quality

• Own day-to-day production issue triage from the support channel through to resolution: reproduce, diagnose, fix, verify, and close the loop with whoever reported it.
• Own output quality of the multi-agent research and qualification pipelines — stale facts, unsupported claims, leaked model reasoning, broken or circular citations, empty result sets.

Delivery & Release Management

• Ship scoped product enhancements end to end — filtering, ownership assignment, bulk import, UI polish, notification and error-surfacing gaps — sized in days, not quarters.
• Own the defined release process (local → dev → staging → production), with staging mirroring production and explicit sign-off before any promotion
• Keep hotfixes surgical — no unreviewed pushes to production

Configuration & QA

• Manage feature flags, tenant configuration, and environment variables across tenants.
• Turn ad hoc QA into something systematic — regression coverage on the connector and workflow surface, pre-demo verification, and a repeatable check before customer-facing releases.
• Execute approved architectural intent faithfully so designs get implemented as specified.

Tools & Technologies

• AI/LLM: multi-agent workflows, prompt and context engineering, tool/function calling, evaluation and guardrails, model cost and latency control (Anthropic and OpenAI experience preferred)
• Platform: durable workflow orchestration, feature flags, multi-tenant architecture
• Enterprise ecosystem: CRM platforms such as ServiceNow, Salesforce, or SAP (ServiceNow a strong plus, not required)

What you bring:

• Experience: 4–8 years of professional software engineering, including meaningful time in a startup or growth-stage environment where you owned production, not just tickets
• AI Practitioner Depth: Demonstrable, hands-on experience building and debugging LLM and multi-agent workflows — prompt/context engineering, tool calling, evaluation and guardrails, cost and latency control
• Technical Foundation: Strong backend and API skills, comfortable in a Git/PR workflow, reading logs and traces, and debugging distributed or workflow-orchestrated systems
• Mindset: Genuine QA instinct — tests their own work, thinks about the regression before shipping, and doesn't confuse "it deployed" with "it works"
• AI Tooling: Fluent with AI coding tools and equally clear on their limits — reviews what the model produced rather than trusting it
• Communication: Direct, low-ego communication; comfortable working daily with non-engineers (product, CSMs, sales) and translating between business and technical language

What you bring:

• Experience with data quality, provenance, citation, or fact-verification systems
• Exposure to MCP (Model Context Protocol) or similar AI integration layers
• Prior work in a CRM, sales technology, or revenue intelligence product
• Comfort supporting live enterprise customers, including in-flight fixes ahead of a customer meeting

Visa Sponsorship

AspenView does not provide visa sponsorship for this role. Candidates must already be legally authorized to work in their country of residence.

Equal Opportunity Employer

AspenView is proud to be an equal opportunity employer. We believe in creating an environment where all employees feel welcome, valued, and empowered to succeed. We celebrate diversity and strive to build a culture of inclusion where all individuals, regardless of their race, color, gender, gender identity or expression, sexual orientation, disability, age, or any other characteristic, can thrive. We encourage applicants from all walks of life to join our team and make a lasting impact.