Artificial Intelligence Engineer

What matters most

• Own the ML and GenAI roadmap

  The posting calls for aligning technical plans, sprint work and delivery timelines with product goals while leading a small team. Prepare an example of how you prioritized work, communicated trade-offs and kept delivery on track.

• Design LLM workflows and agents

  The role leads design of retrieval, tool-use and evaluation pipelines; experience with RAG, memory and agents is required. Prepare to explain architecture choices and how you would test whether each component improves outcomes.

• Ship and operate production systems

  Responsibilities span model CI/CD, APIs, containers, orchestration, monitoring and AWS cost/performance optimization. Prepare an end-to-end deployment example, including observability, version governance and how failures were handled.

• Set quality standards through experiments and reviews

  The posting calls for code, testing and model-evaluation standards, A/B tests, error analysis, and mentoring through code and model reviews. Prepare to show how evidence from experiments informed a decision and how reviews improved team practice.

Likely challenges to think through

• Balancing agent capability with reliability

  Possible challenge: retrieval and tool use can expand what an agent does while introducing incorrect results, unsafe actions or harder-to-debug failures. Prepare to discuss evaluation loops, guardrails and error analysis at each stage of a workflow.

• Controlling production cost without losing quality

  Possible challenge: AWS scale and responsiveness may compete with model quality and operating cost. Prepare to compare measurement-driven options such as retrieval changes, profiling and model optimization; treat fine-tuning and quantization as preferred, not mandatory, approaches.

• Maintaining standards during rapid delivery

  Possible challenge: leading a small team across product, data and infrastructure work may force trade-offs between iteration speed and testing or deployment discipline. Prepare a concrete approach to prioritization, review and stakeholder communication when timelines shift.

Topics to prepare

• Technical leadership and roadmap trade-offs

  The role calls for roadmap ownership, sprint planning, stakeholder communication and mentoring. Suggested exercise: outline a two-sprint plan for an ML feature, explaining scope, dependencies, delivery risks and what you would communicate to product and infrastructure teams.

• ML foundations, transformers and guardrails

  The posting requires understanding of supervised and unsupervised learning, deep learning, transformer architectures, prompt engineering, evaluation and safety guardrails. Suggested exercise: explain when you would use each learning approach, then define quality and safety checks for a transformer-based feature.

• RAG and agent workflow design

  The role explicitly covers retrieval, tools, memory and evaluation loops in LLM workflows. Suggested exercise: diagram a request from retrieval through tool use to response, identifying failure points and what each evaluation would measure.

• Production MLOps and governance

  The posting names model CI/CD, APIs, deployment, observability, infrastructure as code, cost/performance optimization and data/version governance. Suggested exercise: trace how a model change would be tested, released, monitored and rolled back on AWS.

• Knowledge graphs for retrieval

  Neo4j schema design for semantic retrieval and reasoning is preferred; hands-on Neo4j experience is listed as required. Suggested exercise: sketch nodes, relationships and a retrieval path for a small domain, then explain when graph retrieval would add value.

• Fine-tuning and production trade-offs

  Production fine-tuning with LoRA/QLoRA, PEFT, quantization, evaluation, rollback and cost optimization is preferred. Suggested exercise: compare fine-tuning with prompt or retrieval changes for one hypothetical use case, including evaluation, cost and rollback criteria.

Skills to revise

• Write and profile production Python

  Strong Python, including testing, packaging, async I/O and performance profiling, is required. Suggested exercise: package a small asynchronous inference client, add tests and profile a slow path before proposing a fix.

• Build and test a retrieval workflow

  The posting requests hands-on LangChain, Milvus and Neo4j experience alongside RAG and agent workflows. Suggested exercise: prototype retrieval over a small dataset, compare vector and graph results, add a tool-use step and test cases for incorrect retrieval.

• Evaluate models with PyTorch and Hugging Face

  The role guides model development with PyTorch and Hugging Face and sets model-evaluation standards. Suggested exercise: run a small reproducible model comparison, record versioned inputs and metrics, and explain the errors that would block release.

• Plan an observable deployment

  The posting covers AWS, APIs, Docker, Kubernetes, CI/CD and observability, and lists Prometheus as a skill. Suggested exercise: draft a deployment plan with container and API boundaries, release checks, monitoring signals and a rollback trigger.

• Analyze an experiment

  The role works with product, data and infrastructure teams on experiments, A/B tests and error analysis. Suggested exercise: define a primary metric and guardrail for an LLM feature, then classify sample failures and propose a follow-up test.

• Conduct a technical review

  Mentoring and code/model reviews are stated responsibilities. Suggested exercise: review a short hypothetical model-service change for test coverage, evaluation quality, deployment risk and maintainability, then prioritize actionable feedback.

• Map the role to your evidence

  Prepare a one-page evidence map for the stated requirements: at least four years of experience, including one year leading technical projects or teams, plus Python, ML/GenAI, AWS and MLOps. Mark gaps honestly rather than inventing examples.

• Rehearse a leadership walkthrough

  If you have a relevant example, explain how you framed the problem, owned a roadmap, planned delivery, mentored engineers and handled code or model reviews. Bring a concise account of decisions, trade-offs and results.

• Prepare an end-to-end system walkthrough

  Choose a project you can discuss from modeling through API deployment and monitoring. Be ready to explain your contributions, testing, version governance, observability and AWS cost/performance decisions without implying you used tools you did not use.

• Sketch an LLM agent design

  As a practice exercise, diagram retrieval, tool use, memory, evaluation loops and safety guardrails. Explain where LangChain, Hugging Face, PyTorch, Milvus or Neo4j could fit, and how you would test failure cases; label this as a proposed design.

• Bring an evaluation and experimentation plan

  Outline how you would compare a baseline with an LLM workflow using model evaluation, error analysis and, where appropriate, an A/B test. Specify metrics, review of unsafe outputs and what evidence would justify a change.

• Refresh Python and production trade-offs

  Prepare a small code example or discussion covering idiomatic Python, tests, packaging, async I/O and profiling. Practice explaining deployment choices involving CI/CD, APIs, containers, orchestration and infrastructure as code.

• Draft focused employer questions

  Ask what ML/GenAI products the team is prioritizing, how roadmap ownership is shared with product and infrastructure teams, and which deployment or evaluation challenges matter most. Clarify how the permanent role relates to the client mentioned in the posting.