Senior AI Solutions Consultant

Developing production-grade Python code for data processing, custom model development, and model serving. You'll be deep in the code, building the core of our solutions.

Designing and implementing end-to-end training and deployment pipelines using platform-specific SDKs and infrastructure-as-code (e.g., Terraform). You're not just clicking buttons; you're automating the whole process.

Architecting and building CI/CD/CT pipelines for models, ensuring continuous integration, continuous delivery, and continuous training. This means getting models from development to production reliably.

Designing data schemas, writing complex queries to extract and transform data, and implementing data governance rules to ensure our models have good, clean data to work with.

Managing an entire project backlog in Jira, setting up sprint boards, and creating comprehensive architectural and project documentation in Confluence. You're keeping the project organised and transparent.

Creating complex, interactive dashboards that join multiple data sources to report on model performance and business impact. You'll use these to show stakeholders what's happening.

• AI Solutions Manager (your boss, for guidance and strategy)
• Product Management (to define what we're building and why)
• Engineering Teams (who'll help you get your models into production)
• Data Science Peers (for technical collaboration and code reviews)
• Sales & Client Success (who need to understand what you've built to sell and support it)

• Client Project Leads (the people who'll actually use your solution)
• Client Technical Teams (for integration and data access)
• Third-party Vendors (if we're using external tools or services)

Getting a model to work in a notebook is one thing; getting it to run reliably, at scale, and continuously improve in production is another. The gap between POC and production is still huge, and you'll be key to bridging it. Expect more focus on automated testing, monitoring, and robust deployment strategies.

Model monitoring and alerting (drift detection, data quality) · A/B testing for models in production · Containerisation and orchestration (Docker, Kubernetes) · Feature stores and data governance for ML · Cost optimisation for cloud AI infrastructure

• This week: Review the MLOps pipeline of one of our existing production models; identify 2 areas for improvement.
• This month: Take an online course on Kubernetes or a specific MLOps platform (e.g., Kubeflow, Databricks MLflow).
• Month 2: Lead a project to implement automated model monitoring for a new solution.
• Month 3: Propose and implement a cost-saving measure for an existing AI service.

Quick win: Start using Docker for all your local development environments to get comfortable with containerisation.

Data privacy concerns and the need for real-time inference are pushing AI models closer to the data source or the 'edge' (e.g., on devices). This means new architectural patterns and considerations for data locality and security, especially for clients with strict data governance.

Decentralised model training · On-device inference optimisation · Privacy-preserving machine learning techniques · Network latency and bandwidth considerations · Security and trust in distributed AI systems

• This week: Read introductory articles on federated learning and TinyML.
• This month: Explore open-source frameworks like TensorFlow Federated or PySyft.
• Month 2: Identify a potential client use case where edge AI or federated learning could offer a unique advantage.
• Month 3: Develop a small proof-of-concept for an edge-deployed model or a federated learning scenario.

Quick win: Familiarise yourself with the concept of 'model compression' and how it helps deploy models to smaller devices.