ML Platform Engineer (MLOps)

At Docuvera, we’re transforming how the Life Sciences industry creates, manages, and reuses content. As a global SaaS leader in structured content authoring, our mission is to drastically improve time to market, productivity, patient safety, and patient outcomes. Our platform empowers pharmaceutical companies worldwide to drive digital transformation with efficient, compliant, multiformat content creation. Headquartered in Wellington, New Zealand, with a distributed team across New Zealand, Asia, UK / EU, and the US, we support customers all over the globe.

We’re growing and are now looking for an ML Platform Engineer (MLOps) to help design, build, and operate the AI backbone that powers our enterprise‑scale solutions. In this hands‑on role, you’ll collaborate with data scientists and engineers to turn cutting‑edge machine learning into reliable products, ensuring our AI services are scalable, secure, and compliant with life science regulations. You’ll play a key part in delivering capabilities such as intelligent content curation, enterprise knowledge assistants, and AI‑assisted bug triage.

The part you’ll play

• Design, deploy, and scale AI / ML infrastructure on AWS (SageMaker, Bedrock, EKS, ECS, Aurora PostgreSQL, DynamoDB, OpenSearch, Neptune, Lambda).
• Build end‑to‑end MLOps pipelines (SageMaker Pipelines, MLflow, Kubeflow).
• Deploy and optimize vector databases and knowledge graphs for semantic search and reasoning.
• Orchestrate ML lifecycles with EventBridge, SQS, and automation frameworks.
• Ingest and process enterprise data (Confluence, Jira, GitLab, Slack, SharePoint) into reliable workflows.
• Ensure compliance with FDA 21 CFR Part 11 and GxP through governance, audit trails, and encryption.
• Lead model deployment using Docker, Kubernetes, and serverless, with monitoring for drift, quality, and performance.

What you’ll bring

We’re looking for someone with proven experience architecting and tuning AI / ML infrastructure on AWS, strong SQL and PostgreSQL expertise, and hands‑on experience with MLOps platforms such as MLflow, Kubeflow, or SageMaker Pipelines. You’ll bring knowledge of vector search and RAG pipelines, data engineering skills with Glue, Kinesis, and streaming workflows, as well as familiarity with graph databases (Neptune, Neo4j) and their query languages. Proficiency in Python / Bash and Infrastructure-as-Code (Terraform, CDK, CloudFormation), along with strong observability practices using tools like CloudWatch or New Relic, are also key. Bonus points if you hold AWS certifications in Machine Learning, Solutions Architecture, DevOps, or Security.

Just as important as your technical skills, you’re outcome‑focused, proactive, and resourceful, able to balance independence with strong collaboration and communication. You adapt quickly, approach challenges with resilience, and value professionalism and team spirit, celebrating others’ contributions while driving results.

Benefits

We believe people do their best work when they’re trusted, supported, and empowered. You’ll enjoy a digital‑first, fully flexible working style (remote or hybrid), access to cutting‑edge tools and modern AI workflows, and dedicated development time with career growth resources. We offer an extra week of paid leave (at Christmas and your birthday off), all within a supportive, inclusive global team that prioritises collaboration and innovation.

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