Senior Engineer, Machine Learning

What matters most

• Build strategy-focused applications

  The role designs, deploys, and supports Python applications for research, competitive and market intelligence, strategic analysis, and communication. Prepare to explain how a business workflow becomes a usable application and how its value would be assessed.

• Connect agents to dependable data

  The role builds AI agents using approved internal and external sources and maintains pipelines for structured and unstructured data. Prepare to discuss source integration, data quality, and traceability in an analytical workflow.

• Choose and evaluate applied-AI approaches

  The posting calls for practical use of LLMs, RAG, MCP, and agentic frameworks where relevant, plus assessment of output accuracy, relevance, reliability, and business risk. Prepare to justify an approach against simpler alternatives and describe an evaluation method.

• Operate applications with appropriate controls

  Duties extend beyond prototyping to testing, deployment, monitoring, troubleshooting, and access, approval, and human-review controls. Prepare to describe how an application would be maintained and how output-quality issues would be detected and resolved.

• Translate across business and technical teams

  The role defines requirements with strategy, technology, data, security, and legal partners, explains technical limitations, and documents architecture and operating procedures. Prepare to show how stakeholder feedback informs design decisions and sustainable adoption.

Likely challenges to think through

• Speed versus trustworthy analysis

  Likely challenge (inference): Automating research may save time while introducing inaccurate or poorly supported conclusions into decision support. Prepare to discuss source checks, output evaluation, and when human review should precede use.

• Useful access versus data controls

  Likely challenge (inference): Agents need enough internal and external information to be useful, but approved-source, access, security, and privacy boundaries may limit integration. Prepare to explain how to scope data access and handle restricted or low-quality inputs.

• Prototype value versus long-term support

  Likely challenge (inference): A promising prototype may not yet meet usability, scalability, or maintenance needs. Prepare to explain how stakeholder feedback and business value would guide iteration, and what testing, monitoring, and documentation would be needed before wider use.

Topics to prepare

• Choosing an applied-AI architecture

  Compare a direct LLM workflow, RAG, and an agent for a competitive-intelligence question; explain when MCP or an orchestration framework would add value rather than complexity. The posting names these approaches for relevant business use cases, not as a requirement to use all of them.

• AI output quality and business risk

  Draft criteria for accuracy, relevance, reliability, and business risk, then apply them to a short AI-generated market summary with unsupported claims. This reflects the role’s responsibility to judge outputs used in strategic decision support.

• Enterprise controls and failure modes

  Map where source approval, access restrictions, testing, and human review belong in a proposed research workflow; identify what happens when a source is unavailable or an answer cannot be verified. The posting explicitly calls for these controls and sound judgment about security and privacy.

• Strategy-workflow trade-offs

  For a hypothetical research request, state the decision it supports and weigh feasibility, stakeholder value, usability, and scalability before choosing a prototype scope. These are the criteria the role uses to iterate applications for Corporate Development & Strategy.

Skills to revise

• Build and maintain a Python application

  Practice implementing a small Python research application with tests, version-controlled changes, a review-ready change summary, and a troubleshooting note. Python development and software maintenance practices are explicit requirements.

• Integrate structured and unstructured data

  Sketch and implement a small pipeline that ingests a database record and a text document, validates them, and records their source and processing errors. The role develops reliable data pipelines and integrates APIs, databases, and varied information sources.

• Prototype an AI-assisted workflow

  Using only approved sample data, prototype a research-to-summary workflow and show how an agent or RAG approach would retrieve information and produce a reviewable result. This mirrors the advertised AI-agent and applied-LLM work; the particular approach should be justified, not assumed mandatory.

• Test and monitor output quality

  Create a small set of research questions with expected evidence, check generated answers for support and relevance, and define what to monitor or escalate after deployment. The posting calls for AI evaluation, performance and quality monitoring, and issue resolution.

• Explain and document a solution

  Write a one-page design covering requirements, architecture, data flow, limitations, operating steps, and maintenance needs; then explain the trade-offs in plain language for a strategy stakeholder. The role requires cross-functional communication and documentation for sustainable adoption.

• Map the role to evidence

  Prepare a one-page list of examples you could discuss for Python applications, data pipelines, LLM evaluation, and at least one of RAG, MCP, or agent-based development. Note what you personally built, tested, and maintained; the posting identifies these as relevant experience.

• Prepare an end-to-end project walkthrough

  Select an AI application or automation project, if available. Be ready to explain the business need, your contributions, data sources and integrations, design choices, testing, deployment, monitoring, and issues resolved. These reflect the role’s application-development responsibilities.

• Sketch a strategy research design

  As interview practice, diagram a proposed tool that synthesizes market or competitive information from approved sources. Show how structured and unstructured data enters a pipeline, where retrieval or an agent would help, and where a simpler approach might suffice.

• Build an output-quality review plan

  Prepare a small example evaluation set and explain how you would check AI outputs for accuracy, relevance, reliability, and business risk. Include what would trigger human review or a change to prompts, data, or workflow; these are controls named in the posting.

• Explain enterprise operation and safeguards

  Draft a brief architecture and data-flow note covering access, approvals, security and privacy considerations, tests, monitoring, troubleshooting, and maintenance ownership. Treat cloud, containers, and CI/CD as preferred experience rather than mandatory design choices.

• Practice explaining trade-offs to stakeholders

  Prepare a short, non-technical explanation of a prototype’s value, limitations, usability, and scalability, plus how feedback would shape the next iteration. The role works with strategy, corporate development, technology, data, security, and legal teams.

• Write questions for the employer

  Ask which strategy workflows are highest priority, what data sources are approved, how output quality and business value are assessed, and who sets access, approval, and human-review controls. These clarify implementation choices without assuming Seagate’s internal processes.