Job Description

Purpose of Role

The Senior Machine Learning Engineer is the technical anchor for AI across Allan & Gill Gray Philanthropies (AGGP). The role is responsible for designing, building and operating secure, scalable AI solutions on Azure/AWS and within the M365 ecosystem. This includes defining technical standards, leading the implementation of high-value AI use cases, and ensuring that AI tools move from proof-of-concept to reliable, maintainable production systems that support AGGP's philanthropic mission. The Senior Machine Learning Engineer works closely with the Head of AI Strategy & Enablement, the Head of Group IT, the Intermediate Engineer, entity tech leads, and AI Builders embedded in entities.

Objectives of Role

Cloud & AI Architecture

Own reference architectures for LLM-powered applications, copilots and agents, including use of embeddings, vector search, retrieval-augmented generation and orchestration frameworks Design end-to-end AI/ML solutions that integrate with AGGP systems and data sources (e.g. Salesforce NPSP, Fluxx, collaboration tools, data warehouse / data lake) Define standard patterns for secure connectivity to internal and external data sources in coordination with Group IT and the Global Data Strategy work

Platform & Infrastructure (Azure / AWS / Hybrid)

Lead the design and implementation of AGGP's hybrid AI platform (Azure, AWS or hybrid), in line with the AI roadmap and IT strategy. Define and oversee AI risk management: develop internal governance frameworks to manage AI risks: model and tool eval standards; bias, safety and data-privacy checks; incident logging and response. Implement a generic orchestration platform (e.g. LangChain-style stack or equivalent) including capabilities such as prompt management, evals, logging, telemetry and cost monitoring. Work with Group IT to ensure the platform meets baseline security, identity, access management and compliance requirements.

Solution Delivery: From POC to Production

Translate priority AI use cases into technical designs, estimates and implementation plans. Lead the development of AI prototypes and minimum viable products (MVPs), then harden them into production-ready tools and workflows. Ensure robust CI/CD pipelines, infrastructure-as-code, backup/restore and disaster recovery for AI systems. Provide a single point of technical accountability for AI tools in production across entities.

Quality, Safety & Reliability

Implement monitoring, logging and alerting for AI applications, including model-level and system-level metrics. Define and apply evaluation (eval) strategies for AI systems, including accuracy, robustness, bias, safety and performance. Work with the Head of AI Strategy & Enablement and AI Working Group to align technical guardrails with AI risk, ethics and data policies (e.g. handling of beneficiary and grantee data, data classification).

Data & Integration

Work with data teams to connect priority data sources (SharePoint, OneDrive, Outlook, calendar, data warehouse/lake) into AI workflows in a governed way. Design and maintain data pipelines needed for AI tools (e.g. ingestion, preprocessing, indexing, vector stores) in collaboration with Global Data Strategy initiatives.

Technical Leadership, Mentorship & Standards

Set coding, testing and documentation standards for AI-related development. Review and guide the work of the Intermediate AI Engineer and support AI Builders where deeper technical input is required. Contribute to AI training content for technical teams and support internal communities of practice around AI engineering and cloud.

Vendor, Tool & Cost Management

Lead technical evaluation of AI platforms, APIs, vector databases and related tools against AGGP's criteria for security, privacy, performance and cost. Provide input into AI license and infrastructure decisions (e.g. ChatGPT Enterprise, Copilot for M365, coding assistants, avatar tools), aligning with the AI budget and adoption targets. Optimise cloud and API usage to stay within budget while supporting experimentation and growth.

Experience and Qualifications

Education:

Bachelor's degree in Computer Science, Engineering or related field, or equivalent practical experience.

Experience:

5-8 years of experience in software or cloud engineering, with at least 3 years designing and operating systems on Azure and/or AWS. Demonstrated experience building and shipping production software (web apps, APIs, services) using modern languages and frameworks. Practical experience with LLMs and/or machine learning (e.g. calling model APIs, fine-tuning, retrieval-augmented generation, vector search). Experience working with or integrating into enterprise SaaS platforms (e.g. Salesforce, grant management systems, M365). Experience collaborating with cross-functional teams and non-technical stakeholders; experience in nonprofit or social impact settings is a plus. Technical team - leadership experience

Competencies

Technical Proficiency:

Strong skills in a modern backend language (e.g. Python, TypeScript/Node.js or similar). Solid understanding of: + LLM concepts (prompts, embeddings, context windows, evals).
+ Cloud native design, security, networking and identity.
+ CI/CD, infrastructure as code (e.g. Terraform, Bicep, CloudFormation).
Familiarity with: + Orchestration frameworks (e.g. LangChain style, Flow/graph based tools).
+ Vector databases / search.
+ Monitoring and logging stacks (e.g. Application Insights, CloudWatch, ELK).

Behavioural Competencies and Technical Skills:

Mission-driven and aligned with AGGP's ethics statement: ambitious about positive impact, cautious about harm, and committed to transparency and human accountability. Able to translate complex technical trade-offs into clear choices for non-technical stakeholders. Strong ownership mindset; comfortable being the technical point person for critical AI systems. * Pragmatic and experimental: able to prototype quickly while keeping an eye on long-term maintainability.