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.
Preferred
- 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.
Want to learn more about SC&E Check us out on our platform: http://www.wwt.com/consulting-services-careersCertain states and localities require employers to post a reasonable estimate of salary range. A reasonable estimate of the current base pay range for this position is $104,000 to $130,000 annually. Actual salary will be based on a variety of factors, including shift, location, experience, skill set, performance, licensure and certification, and business needs. The range for this position in other geographic locations may differ. Certain positions may also be eligible for variable incentive compensation, such as bonuses or commissions, that are not included in the base pay.
The well-being of WWT employees is essential. When it comes to our benefits package, WWT has one of the best. We offer the following benefits to all full-time employees:
- 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
Note: This is not an all-encompassing list and should not be used as a complete description of the plan's benefits. For more information, see our US benefits website at wwt.com/us-benefits.
RESPONSIBILITIES:- 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, and the 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, and 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.
