Machine Learning Engineer

Q2•Austin, TX

6d•Hybrid

About The Position

The Machine Learning Engineer designs and evolves enterprise AI systems and architectures that enable scalable, secure, and high-impact adoption across the organization. This role defines end-to-end AI solution patterns involving LLMs, APIs, RAG, vector search, intelligent agents, orchestration workflows, Snowflake, cloud platforms such as AWS and Azure, and enterprise data integration. The role partners across data, engineering, business applications, operations, IAM, and governance teams to create reusable frameworks that accelerate delivery while supporting security, access controls, compliance, and audit needs. This position may require minimal travel for collaboration, planning, or stakeholder engagement.

Requirements

5–8 years of relevant professional experience in engineering, architecture, data, or enterprise technology roles
Bachelor’s degree in a relevant field.
Strong understanding of enterprise architecture principles, system design, integration patterns, and scalable delivery models.
Experience partnering with business and technical stakeholders to define practical solutions for complex organizational needs.
Ability to evaluate ambiguous problems, structure recommendations, and communicate trade-offs clearly.
Demonstrated judgment in balancing innovation, security, governance, cost, and operational feasibility.
Strong collaboration skills with the ability to influence across teams without direct authority.
Working knowledge of responsible technology adoption, risk management, and enterprise control environments.
Fluent written and oral communication in English.
Authorized to work for any employer in the U.S.
Unable to sponsor or take over sponsorship of an employment Visa at this time.

Responsibilities

Design enterprise solution patterns that align business needs with scalable implementation approaches.
Collaborate with cross-functional teams to translate business challenges into structured architecture recommendations.
Identify, analyze, and resolve gaps related to scalability, data readiness, interoperability, and operational adoption.
Define reusable frameworks and standards that improve delivery consistency across teams and use cases.
Evaluate solution options, assess trade-offs, and provide recommendations that balance performance, cost, risk, and business value.
Guide teams through architecture decisions that support responsible adoption and long-term maintainability.
Influence organizational direction by promoting best practices, shared patterns, and practical governance approaches.
Stay current on emerging practices and assess their applicability to enterprise priorities.