Augmented Engineer (AI)

About Eudia: Eudia is redefining the future of legal work with AI-powered Augmented Intelligence. Enabling Fortune 500 legal teams to move faster, manage risk more effectively, and unlock new business value. Backed by $103M in Series A funding led by General Catalyst, we're building a category-defining platform that blends AI-driven automation with human expertise, transforming legal from a cost center into a strategic growth driver.

At Eudia, we move fast. Unlike traditional enterprise software, our teams ship solutions in days, not months-delivering real impact for some of the world's largest companies, including Cargill, Coherent, DHL, and DuPont. We're solving one of the most complex, unsolved challenges in AI: bringing trust, accuracy, and security to legal automation.

We're a team of builders, operators, and problem-solvers who are passionate about reshaping an industry that has long been resistant to change. If you're looking for a place where you'll be challenged, take ownership from day one, and work alongside some of the brightest minds in AI and legal-we'd love to meet you.

Augmented Engineer (AI) As anAugmented AI Engineer, you will work hands-on with clients to deploy, optimize, and scale AI-powered solutions that address mission-critical problems. Combining expertise in AI engineering with exceptional problem-solving and client-facing skills, you will bridge the gap between our cutting-edge AI technologies and real-world applications. This role demands technical proficiency in machine learning, adaptability, and a passion for delivering impactful AI solutions in dynamic environments.

Key Responsibilities:

• AI Solution Deployment: Deploy, configure, and fine-tune AI models, algorithms, and platforms to meet client-specific use cases, ensuring seamless integration with existing systems.
• Model Development & Optimization: Build, train, and optimize machine learning models (e.g., LLMs, computer vision, predictive analytics) tailored to client data and goals.
• Data Pipeline Engineering: Design and implement robust data pipelines to process and transform structured and unstructured data for AI model training and inference.
• Technical Problem-Solving: Diagnose and resolve complex AI-related issues, such as model performance bottlenecks or data quality challenges, often under tight deadlines.
• Client Collaboration: Partner with clients to understand their business objectives, data challenges, and operational needs. Translate these into AI-driven technical requirements.
• Feedback Loop: Collaborate with internal AI research and engineering teams to relay client feedback, driving improvements to models and platforms.
• AI Innovation:Identify opportunities to enhance client outcomes through advanced AI techniques, such as reinforcement learning, generative AI, or real-time inference.

Qualifications:

• Education: Bachelor's or Master's degree in Computer Science, Data Science, AI, or a related field.
• Technical Skills:
• Proficiency in Python and AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Experience with large language models (LLMs), computer vision, or other advanced AI techniques.
• Strong knowledge of data engineering (SQL/NoSQL, ETL pipelines, data lakes).
• Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps tools (e.g., Kubeflow, MLflow).
• Experience with APIs, microservices, and deploying AI models at scale.
• Problem-Solving: Ability to tackle ambiguous AI challenges and deliver practical, high-impact solutions.
• Communication: Exceptional ability to explain complex AI concepts and model outputs to non-technical stakeholders.
• Adaptability: Thrives in fast-paced, client-facing environments with evolving requirements.
• Travel: Willingness to travel to client sites as needed (up to [X]% of the time, depending on role requirements).
• Experience: 4+ years in AI engineering, machine learning, or client-facing technical roles. Experience deploying AI solutions in production is a plus.

Preferred Qualifications:

• Experience in industries like healthcare, finance, defense, or logistics, where AI drives decision-making.
• Familiarity with generative AI, reinforcement learning, or real-time AI inference systems.
• Prior experience in consulting or deploying AI solutions in a SaaS/enterprise environment.
• Knowledge of DevOps practices for AI (e.g., CI/CD for ML models, containerization with Docker/Kubernetes).

$130,000 - $200,000 a year

If you're ready to take on the challenge and make an impact in a rapidly evolving industry, we want to hear from you. Apply today with your resume and a cover letter explaining why you're the perfect fit for this role.