AI Engineer

Role Summary

As an AI Engineer at OneMagnify, you’ll focus on making machine learning and generative AI systems reliable, scalable, and production‑ready for real client use. You’ll sit within our AI team and work closely with data scientists, engineers, and delivery partners to operationalize models across digital, analytics, and marketing platforms. This role is designed for someone with professional experience who enjoys hands‑on engineering work and wants to deepen their impact at the intersection of AI, infrastructure, and real‑world business outcomes.

The Impact You’ll Have

You’ll play a critical role in helping clients move AI from proof‑of‑concept to production. Your work will ensure machine learning and generative AI models are deployable, observable, and continuously improving—so clients can depend on them to inform decisions, power applications, and enhance customer experiences.

At OneMagnify, AI is delivered as part of integrated solutions. You’ll collaborate across data science, engineering, strategy, and analytics teams to embed MLOps practices into broader CRM programs, digital platforms, and marketing ecosystems. Your work ensures AI systems don’t just exist—but operate reliably within complex, real‑world environments.

What You’ll Do

• Build and Maintain Production ML/AI Pipelines
  Design and maintain production ML and AI pipelines, including training, evaluation, deployment, and monitoring. Create data pipelines and prepare datasets for AI consumption. Develop scalable model serving architectures for real‑time and batch inference. Ensure AI and LLM systems are production‑ready, stable, and performant.
• Apply MLOps Best Practices
  Implement experiment tracking, model versioning, and reproducible training workflows. Establish processes for continuous evaluation and improvement of machine learning and generative AI systems. Support reliable lifecycle management of models from experimentation through deployment and iteration.
• Monitor, Observe, and Improve Models in Production
  Build and maintain monitoring systems to detect data drift, performance degradation, and operational issues. Use evaluation frameworks and monitoring signals to guide model improvements over time. Help teams respond to production issues with clear diagnostics and remediation approaches.
• Be a Trusted Technical Partner to Clients
  Work directly with clients to explain AI concepts, tradeoffs, and outcomes in clear, practical terms. Contribute to strong client relationships through thoughtful solutioning and consistent delivery. Present technical approaches and results to both technical and non‑technical stakeholders.
• Collaborate Across Integrated Teams
  Work closely with data scientists and AI engineers to operationalize models effectively. Partner with software engineers to ensure smooth deployment and integration into client platforms. Collaborate with analytics, strategy, and delivery teams to align MLOps solutions with client objectives.
• Support Client-Facing Delivery
  Contribute to client discussions by explaining MLOps approaches, tradeoffs, and outcomes in practical terms. Help translate client requirements into operational AI solutions that can scale and evolve. Support consistent, high‑quality delivery across multiple client engagements.

What You’ll Need

• Bachelor’s degree in a relevant field or equivalent practical experience; Master’s degree preferred.
• 2+ years in a technical role focused on machine learning, data platforms, or AI systems (2+ years post‑Master’s if applicable).
• Hands-on experience deploying and operating machine learning or generative AI models in production environments.
• Strong understanding of MLOps practices, including experiment tracking, model versioning, and monitoring.
• Experience building data and model pipelines in distributed environments.
• Familiarity with model evaluation frameworks and performance monitoring techniques.
• Exposure to large language models and applied AI use cases.
• Strong object-oriented programming skills.
• Working knowledge of Databricks.
• Proficiency with Python, SQL, and related analytics or engineering tools; familiarity with BI tools such as Tableau, Power BI, or Domo is a plus.
• Experience owning or leading technical workstreams in collaborative environments.
• Clear communication skills, including the ability to explain technical concepts to non‑technical stakeholders.
• Experience in integrated marketing, digital agency, marketing services, or consulting environments preferred.

Future‑Ready Skills (Nice to Have)

• Experience operationalizing generative AI or LLM‑based systems in client-facing or production environments.
• Familiarity with marketing technology stacks, CRM platforms, or customer data platforms.
• Exposure to automation, CI/CD for ML, or model orchestration workflows.
• Experience supporting platform‑based or reusable delivery models across clients.
• Comfort working in environments where AI systems support business and customer experience outcomes.

Benefits

We believe great work happens when people have the support and flexibility they need to thrive. Our benefits include medical, dental, and vision coverage, a 401(k) retirement plan, paid holidays, and Flexible Time Off (FTO) so you can take time away to recharge when you need it. We also offer additional programs focused on wellness, financial security, and professional growth.

Equal Opportunity Employer

We believe that Innovative ideas and solutions start with unique perspectives. That’s why we’re committed to providing every employee a workplace that’s free of discrimination and intolerance. We’re proud to be an equal opportunity employer and actively search for like-minded people to join our team.

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform job functions, and to receive benefits and privileges of employment. Please contact us to request accommodation.

Key skills/competency

• AI Engineer
• Machine Learning
• Generative AI
• MLOps
• Python
• SQL
• Databricks
• Data Pipelines
• Model Deployment
• Client Communication