SATIC: Forward Deployed Engineer

Management Level

Job Description & Summary

Focused on relationships, you are building meaningful client connections, and learning how to manage and inspire others. Navigating increasingly complex situations, you are growing your personal brand, deepening technical expertise and awareness of your strengths. You are expected to anticipate the needs of your teams and clients, and to deliver quality. Embracing increased ambiguity, you are comfortable when the path forward isn’t clear, you ask questions, and you use these moments as opportunities to grow.

Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to:

• Respond effectively to the diverse perspectives, needs, and feelings of others.
• Use a broad range of tools, methodologies and techniques to generate new ideas and solve problems.
• Use critical thinking to break down complex concepts.
• Understand the broader objectives of your project or role and how your work fits into the overall strategy.
• Develop a deeper understanding of the business context and how it is changing.
• Use reflection to develop self awareness, enhance strengths and address development areas.
• Interpret data to inform insights and recommendations.
• Uphold and reinforce professional and technical standards (e.g. refer to specific PwC tax and audit guidance), the Firm's code of conduct, and independence requirements.

About the Agentic Innovation Lab

The Innovation Lab serves as SATIC’s AI engineering hub. It focuses on solving complex  business problems by designing, building, and scaling AI-powered solutions (Generative AI,  Agentic AI, and intelligent automation) to transform business operations.

The Role

This position sits at the intersection of legal domain expertise and applied AI engineering. The role is "forward-deployed", meaning the engineer embeds directly with client teams to diagnose bottlenecks, map workflows, and build solutions within the client’s environment. Between deployments, the engineer works from the Innovation Lab to refine internal tools and contribute to a reusable asset library.

Key responsibilities

1. Solution Architecture and AI Engineering

• Deconstruct workflows into machine-readable logic; 
• Build RAG pipelines (embedding models, vector databases, retrieval tuning); 
• Architect multi-agent systems; and 
• Maintain taxonomies and metadata schemas.

2. Engineering, Prototyping & Production Delivery

• Rapidly iterate AI prototypes; 
• Integrate AI with enterprise platforms (CLM, document repositories, APIs); and 
• Establish evaluation frameworks for accuracy, latency, and cost.

3. Client Engagement & Forward Deployment

• Embed on-site for 2–6-week sprints; 
• Manage concurrent engagements; 
• Act as a technical translator between stakeholders and engineering teams; and 
• Deliver technical demonstrations.

4. Risk, Governance & Quality Assurance

• Implement guardrails against hallucinations and bias; 
• Ensure compliance with data privacy (GDPR, POPIA) and AI governance frameworks (EU 
AI Act, ISO/IEC 42001, NIST AI RMF); and 
• Maintain audit trails.

5. Team Contribution & Knowledge Building

• Manage technical backlogs (Jira/Azure DevOps); 
• Mentoring junior associates; and 
• Contribute to the shared asset library.

Experience & Skills

Domain Expertise

• Bachelor’s degree (Economics, Law, Computer Science, Data Science, Information 
Systems, or equivalent); 
• 4–8 years of experience in Business Engineering, Operations, or Product Development; 
• Ability to map complex business processes.

Engineering and AI

• Experience with at least one LLM orchestration framework.
• Working knowledge of at least one major cloud platform.
• Experience integrating AI solutions with enterprise systems.

Communication

• Ability to explain technical AI concepts to business professionals and deliver polished 
demonstrations to senior stakeholders

Desired Skill sets

• Experience with AI evaluation and observability tools.
• Hands-on configuration experience with CLM platforms or workflow automation tools 
• Familiarity with AI governance frameworks: EU AI Act risk classification, ISO/IEC 42001, 
NIST AI RMF, or responsible AI principles.
• Experience with fine-tuning LLMs, training custom embedding models, or building  custom evaluation datasets.
• Solid understanding of Agile/Scrum delivery and legal professional privilege.

Soft skills and innovation mindset

• Builder Instinct: Prioritizes functional prototypes while maintaining good 
documentation.
• Comfort with Ambiguity: Capable of creating structure in uncertain environments.
• Rigorous and Precise: Disciplined in testing and ensuring accuracy in enterprise AI.
• Collaborative: Values cross-functional input and team effectiveness.
• Intellectually Honest: Transparent about knowledge gaps and collaborative in seeking help

Travel Requirements

Available for Work Visa Sponsorship?

Job Posting End Date