Machine Learning Engineer II

Job Summary

The Machine Learning Engineer II supports the discovery, design, and delivery of AI- and automation-enabled solutions that improve operational workflows. The role partners with business stakeholders to understand pain points, build practical prototypes, integrate tools and systems, and support the adoption of solutions that deliver measurable efficiency and quality improvements. This position operates with moderate independence on defined initiatives and contributes to scalable, secure, and auditable workflow improvements.

Responsibilities

Workflow Discovery and Prioritization

Partner with stakeholders to understand current workflows, pain points, and opportunities for simplification or automation.
Document process observations, summarize requirements, and support prioritization of high-impact initiatives.
Contribute to a practical roadmap by highlighting value, effort, dependencies, and implementation considerations.

Solution Design and Build

Design and build practical AI and automation solutions for defined business workflows.
Automate repetitive work such as intake, triage, reporting, and knowledge access.
Prototype quickly, test with users, and iterate based on feedback to improve usability and adoption.
Support integrations with existing systems such as M365, intake tools, and document repositories using APIs and authentication frameworks.

AI and Automation

Apply AI and generative AI to document-centric use cases and operational workflow improvement.
Support retrieval-augmented generation (RAG) solutions, including document ingestion, parsing, semantic chunking, embeddings, and retrieval patterns.
Evaluate tools and recommend production-ready use cases that fit approved governance, risk, and security requirements.
Incorporate appropriate human review into solutions where accuracy, auditability, and defensibility are required.

Adoption and Enablement

Create clear documentation, usage notes, and playbooks to support implementation and adoption.
Train team members on new tools and workflows and reinforce day-to-day usage.
Track basic success metrics such as usage, time saved, and error reduction.
Support the evolution of how work is delivered through practical, repeatable process improvements.

Cross-Functional Partnership

Work with Legal Ops, IT, Security, Data, and Compliance partners to deliver secure and scalable solutions.
Translate business needs into technical requirements and communicate progress clearly to non-technical stakeholders.
Collaborate with control functions to ensure solutions meet data privacy, regulatory, and governance standards.

What Success Looks Like

Within the first year you will have:

Reduced manual legal work through automation.
Built scalable intake and workflow processes.
Improved visibility into Legal's work through dashboards and reporting.
Increased self-service for internal business clients.
Leveraged AI responsibly to improve team productivity.
Helped transform Legal into a data-driven business partner.

Requirements

Technical and Systems Proficiency

Strong programming fundamentals in Python or a similar language.
Experience integrating systems via APIs and supporting system-to-system workflows.
Experience building or supporting LLM-powered applications, including RAG-based systems.
Familiarity with document ingestion pipelines, transformation, semantic chunking, embeddings, vector storage, and retrieval techniques.
Experience with automation tools such as Retool, Zapier, or UiPath is preferred.
Exposure to cloud and data platforms such as AWS or Snowflake is a plus.

Experience

Minimum 2 years of related experience in software, automation, data, or AI environments.
Experience translating real-world business problems into technical solutions.
Experience working in environments where behavior change and adoption are required for success.

Ways of Working

Operates with ownership on assigned work while seeking guidance on broader priorities and escalations.
Prioritizes high-impact work and maintains focus on practical delivery.
Communicates clearly with non-technical stakeholders and builds productive working relationships.
Supports others through documentation, training, and steady follow-through.

Language Skills

Excellent written and verbal communication skills with the ability to communicate effectively across all organizational levels.
Ability to facilitate discussions, influence stakeholders, and communicate complex operational concepts in a clear and actionable manner.

Education/Certifications

Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.