Principal Software Engineer, AI Engineering

What matters most

• Lead AI architecture and technical standards

  The role is the AI engineering team’s technical authority and calls for exemplary designs, architecture and code, plus code reviews. Prepare to explain architectural decisions you have led and how you made them usable by other engineers.

• Turn product needs into model execution

  The engineer works with development teams and the product owner to define and estimate requirements, break them into software tasks, and implement model execution. Prepare a concise example of translating an ambiguous business need into a deliverable design.

• Build business-specific LLM capabilities

  The posting calls for fine-tuning models, developing RAG systems, and using LLM APIs and prompts for conversational AI and machine-driven insights. Prepare to explain when you would choose each approach and how you would assess whether it serves the use case.

• Deliver maintainable platform services

  The role develops and maintains AI integrations and services for the Immutably™ Platform and leads discussions on scaling and performance. The requirements emphasize JVM development, containerized microservices, Kubernetes, databases, and testing. Prepare to connect implementation choices to operability and maintenance.

• Raise the team’s technical capacity

  The engineer will mentor less senior colleagues, lead technical talks, work in an agile team, and coordinate across time zones. Prepare examples of improving engineering decisions through reviews, coaching, and clear asynchronous communication.

Likely challenges to think through

• Choosing the right LLM approach

  A likely design challenge is deciding whether prompt optimization, RAG, or fine-tuning best addresses a business case without adding unnecessary complexity. Prepare a decision framework covering evidence of quality, data needs, iteration effort, and maintenance; the posting does not specify a preferred approach.

• Balancing AI performance with service maintainability

  Because the role spans model execution, scalable platform services, and ongoing maintenance, improving AI behavior may compete with latency and operational simplicity. Prepare to discuss how you would measure those trade-offs and isolate model-dependent components; no specific performance problem is stated.

• Aligning decisions across teams and time zones

  Collaboration with a product owner, other development teams, and a distributed team could make requirements and architectural decisions harder to keep consistent. Prepare an example of documenting decisions, resolving disagreements, and maintaining momentum asynchronously; this is a coordination hypothesis, not a reported company issue.

Topics to prepare

• LLM API and prompt design

  The posting requires LLM API integration for conversational AI and machine-driven insights, plus prompt optimization. For revision, diagram a request flow and compare two prompts against the same example inputs, explaining how you would judge response quality.

• Fine-tuning and RAG trade-offs

  The role explicitly includes fine-tuning models and developing retrieval-augmented generation (RAG) systems for business cases. For revision, take one hypothetical business requirement and explain when you would use fine-tuning, RAG, or both, including how you would evaluate the result.

• Scalable AI-service architecture

  The engineer is expected to provide architectural guidance and lead discussions on building, scaling, performance, and maintenance. For revision, sketch an AI service within the Immutably™ Platform and identify likely performance bottlenecks and maintenance boundaries; the sketch is a preparation exercise, not a stated platform design.

• Data-store choices

  The posting calls for proficiency with relational databases such as MySQL or PostgreSQL and experience with NoSQL or NewSQL databases. For revision, compare where each type might fit in a hypothetical AI integration and explain the consistency and query needs behind your choices.

• Principal-level technical leadership

  The role includes translating requirements with a product owner, reviewing code, mentoring engineers, and coordinating across time zones. For revision, rehearse how you would break an ambiguous AI requirement into estimated tasks and communicate an architectural decision for asynchronous review.

Skills to revise

• Implement a JVM-based LLM integration

  The posting requires strong Java or other JVM-language coding, Java-framework experience, API integration, and conversational AI delivery. As practice, implement a small Java or Scala service endpoint that invokes a mock LLM API, handles failures, and returns a conversational response; use a listed framework if useful.

• Build and check a RAG flow

  Developing RAG systems is an explicit responsibility. As practice, assemble a small set of sample documents, retrieve relevant passages for test questions, generate grounded answers, and inspect cases where retrieval or answers fail. This is a suggested exercise, not a specified company workflow.

• Run a focused fine-tuning exercise

  The posting requires fine-tuning pretrained language models for specific tasks. As practice, define a small task-specific example set, compare model behavior before and after fine-tuning, and explain limitations in the comparison; no particular model or training tool is specified.

• Package and deploy a microservice

  Docker-based microservices and Kubernetes are stated requirements. As practice, package the JVM service in Docker, draft its Kubernetes deployment configuration, and walk through how you would diagnose a failed rollout.

• Test and review AI-service code

  Unit and functional testing, source code management, and code reviews are explicitly named. As practice, add tests for prompt construction, API failures, and an end-to-end service request; then review a change set for correctness and maintainability, explaining your feedback.

• Draft a requirements-to-execution example

  Prepare a one-page example of turning a business request into estimated software tasks and model execution. The role calls for this work with development teams and a product owner; use a hypothetical request rather than assuming a specific Immutably™ use case.

• Prepare an AI project walkthrough

  Choose a relevant project, if available, and outline your decisions on LLM API integration, prompts, fine-tuning or RAG, testing, deployment and results. Distinguish what you built from what the wider team delivered.

• Practice an AI-system design exercise

  Sketch a hypothetical service for conversational AI or machine-driven insights. Be ready to explain a Java or JVM implementation, API boundaries, database choices, Docker and Kubernetes deployment, and trade-offs in scaling, performance and maintenance.

• Assemble engineering-quality evidence

  Select examples you can discuss of unit and functional tests, source-code practices and code reviews. Prepare to explain a design or code change you recommended and how you evaluated it.

• Rehearse a principal-level leadership example

  Outline one example of leading a technical discussion or mentoring engineers, including how you handled architectural disagreement and coordinated work across teams or time zones.

• Write questions for the employer

  Ask how AI work connects to the Immutably™ Platform and analytics products, which business cases currently drive fine-tuning or RAG, and how the team evaluates and maintains deployed AI systems.