[Hiring] MLOps Engineer @World Wide Technology

Role Description

• Develop, productionize, and deploy cutting-edge AI & ML solutions inside client environments; both cloud and on-prem.
• Build and operationalize the infrastructure models run on:
  • Kubernetes clusters
  • GPU management
  • Model serving
  • AI gateways
  • CI/CD pipelines that promote models between environments
• Design and build ML pipelines:
  • Feature extraction and transformation
  • Training and retraining jobs
  • Model registry
  • Validation gates
  • Deployment at scale
• Deploy solutions with enterprise rigor:
  • Infrastructure as code
  • Observability
  • Guardrails
  • Cost controls
• Proactively identify issues with production readiness, security, or architecture reviews, and develop solutions.
• Work in cross-functional agile teams with WWT data scientists, data engineers, engagement managers, etc. from scoping through delivery.
• Coach teammates on software delivery excellence (version control, automated testing, release management, and environment hygiene) and AI-native engineering (coding agent use, harness engineering, MCP servers, spec-driven development).
• Bring what you learn on engagements back to the practice as reusable patterns, reference architectures, and accelerators.
• Support pre-sales scoping and internal R&D.
• Stay current on new developments in AI (models, techniques, tooling and platforms) and provide pragmatic advice to teammates and clients.

Qualifications

• Experience deploying AI and ML systems in production.
• Experience working on project-based or consulting teams that deliver into client environment is preferred.
• Hands-on Kubernetes experience in production: Helm, ingress, persistent storage, autoscaling, and GPU scheduling.
• Working depth in at least one major cloud (AWS, Azure, GCP): provisioning, IAM, networking, autoscaling, and its managed AI/ML services.
• Understanding of how LLM systems behave in production: model serving and quantization, GPU memory sizing, vector databases, RAG components, and gateway and guardrail layers.
• Regular use of AI coding agents in your own production work, and experience shaping how they behave: writing tool and MCP server definitions, maintaining repository context files, building eval suites, and setting guardrails.
• Infrastructure as code and CI/CD: Terraform or Pulumi, Ansible, GitHub Actions or GitLab CI or Azure DevOps, container builds, trunk-based development, and test-driven development.
• Experience with MLOps tooling and platforms: MLflow, Kubeflow, model registries, feature stores, and at least one of Databricks, SageMaker, Azure ML, Vertex AI, Domino, or Dataiku.
• Observability for ML and LLM workloads: Prometheus and Grafana, OpenTelemetry, LLM tracing tools (LangFuse, LangSmith, Arize, or similar), drift monitoring, and cost tracking.
• Experience with common data science languages; Python, SQL, and shell scripting.
• Knowledge of the ML lifecycle (data wrangling, model selection, training, validation, deployment, retraining) and experience working day to day with data scientists.
• Familiarity with cloud data platforms such as Snowflake, Databricks, or Microsoft Fabric.
• Clear written and spoken communication with teammates, client engineers, and executives.
• Comfortable presenting architecture decisions and tradeoffs, and experience mentoring other engineers.

Requirements

• On-prem or hybrid infrastructure experience: GPU servers, the NVIDIA software stack (drivers, CUDA, NIM, Triton, AI Enterprise), and the storage and networking that training and inference workloads demand.
• Experience influencing and building mindshare convincingly with any audience.
• Confident and experienced in public speaking.
• Ability to communicate complex ideas in a concise way.
• Fluent with popular diagraming and presentation software.

Benefits

• Health and Wellbeing: Health (Medical & Prescription), Dental, and Vision Care, Onsite Health Centers (MO & IL), Employee Assistance Program, Wellness program.
• Financial Benefits: Competitive Pay, Profit Sharing, 401k Plan with Company Matching, Life and Disability Insurance, Flexible Spending Accounts, Tuition Reimbursement.
• Paid Time Off: PTO & Holidays, Parental Leave, Medical Leave, Military Leave, Bereavement, Day of Caring.
• Additional Perks: Family Planning Benefits, Nursing Mothers Benefits, Voluntary Legal, Voluntary Supplemental Accident/Illness/Hospital, Voluntary ID Theft, Pet Insurance, Employee Discount Program.