Principal, AI Engineer

What matters most

• Own the shared AI platform

  The role designs and writes production code for the enterprise platform on Databricks on Azure, including model serving, a multi-LLM gateway, and deployment infrastructure. The gateway must handle routing, cost tracking, rate limiting, and failover.

• Make retrieval and agents reusable

  Build hybrid RAG services spanning vector search, structured Delta-table extraction, and analytical, semantic, or hybrid query routing. Develop agent orchestration, a model registry, prompt library, and agent-to-agent workflows that can serve multiple business use cases.

• Enforce information barriers

  Implement MNPI controls and deal-context separation at retrieval and inference time, using Unity Catalog governance, retrieval permissions, access controls, and audit logs. Produce technical evidence for architecture review and regulatory sign-off.

• Make releases measurable and reversible

  Establish evaluation, staged rollout, and rollback from sandbox to production. Monitor LLM latency, token spend, retrieval quality, hallucination flags, and cost by use case.

• Set standards across the hub-and-spoke organization

  Define APIs, SDKs, and engineering patterns for investment-vertical teams; review their work and mentor engineers. Partner with AI Product Management and Data Engineering, and document architecture decisions for governance review.

Likely challenges to think through

• Useful answers without crossing deal boundaries

  Preparation hypothesis: Broad retrieval could improve answers while increasing the risk of exposing another deal's MNPI. Prepare to explain how identity, deal context, retrieval permissions, and inference controls work together, including how to test denials and record auditable evidence.

• Shared primitives without blocking vertical teams

  Preparation hypothesis: Common APIs and standards must accommodate distinct deal, portfolio, investor, and legal workflows without becoming overly rigid. Prepare an example of what belongs in the shared platform versus a vertical-specific application, and how you would resolve competing requests.

• Output quality versus latency and spend

  Preparation hypothesis: Hybrid retrieval and multi-stage agents may improve long-document work but add tokens, complexity, and response time. Prepare to explain how evaluation results, routing, token budgets, per-use-case cost data, and staged rollback would guide those trade-offs.

Topics to prepare

• Databricks-on-Azure platform architecture

  Revise how Delta Lake, Unity Catalog, Model Serving, Vector Search and Gold-layer data products fit into a shared AI platform. Exercise: draw a reference architecture showing data, retrieval, serving and deployment boundaries. Relevance: the role owns the enterprise platform and its link to Data Engineering.

• Hybrid RAG for long-form investment documents

  Compare chunking, embedding choice, structured extraction into Delta tables, and analytical versus semantic versus hybrid query routing. Exercise: outline how a CIM review query would select a retrieval path and preserve evidence for its answer. Relevance: production RAG and these routing patterns are explicit requirements.

• Multi-LLM gateway and agent design

  Revise model-routing trade-offs, rate limits, failover and cost tracking alongside multi-stage agents, schema-constrained outputs and token budgets. Exercise: sketch a gateway and an orchestrator–section–editor flow for IC memo drafting, including failure paths. Relevance: both are named platform capabilities.

• MNPI barriers and regulated AI governance

  Revise deal-context separation, retrieval permissions, inference-layer controls and audit logging. Exercise: trace an unauthorized cross-deal request through the controls and list the evidence an architecture review would need. Relevance: the posting explicitly calls for MNPI controls and technical evidence for governance sign-off.

• Evaluation, rollout and observability

  Revise how retrieval quality, generation accuracy, hallucination flags, latency, token spend and per-use-case cost inform staged rollout and rollback. Exercise: define a small release scorecard and rollback triggers for an LP-reporting use case. Relevance: these measures and deployment safeguards are stated responsibilities.

• Hub-and-spoke engineering standards

  Revise the boundary between reusable platform services and investment-vertical use cases. Exercise: draft an architecture decision record explaining an API or SDK standard, its trade-offs and how vertical engineers would adopt it. Relevance: the principal sets cross-team standards, reviews designs and produces ARB-ready artifacts.

Skills to revise

• Implement an asynchronous Python LLM API

  Practice writing a small Python API service with asynchronous LLM SDK calls, prompt templating, rate limiting, routing and failover; test timeout and cost-accounting paths. Relevance: Python APIs, async integration and multi-LLM gateway operation are explicit requirements.

• Prototype a permission-aware retrieval service

  Using sample nonconfidential documents, practice chunking, embedding, hybrid retrieval and structured extraction; filter results by a simulated deal context before generation. Relevance: the role builds hybrid RAG services and enforces retrieval-layer information barriers.

• Configure governed Azure platform access

  Practice mapping Unity Catalog permissions, Entra ID identities, Key Vault secrets and audit events for separate use cases; verify denied access as well as allowed access. Relevance: Databricks-on-Azure governance and access controls are central to the posting.

• Build a constrained multi-agent workflow

  Implement a small multi-stage drafting flow with schema-validated outputs, token budgets and an explicit handoff between agents; test malformed outputs and excessive verbosity. Relevance: the platform must provide reusable agent orchestration and output-quality controls.

• Package an evaluated release and review artifact

  Practice versioning a model and prompts with MLflow or an equivalent, running a retrieval and generation evaluation in a CI/CD pipeline, and documenting staged rollout, rollback and monitoring in a short decision record. Relevance: registry management, deployment pipelines, evaluation and governance documentation are specified.

• Map evidence to the role

  Prepare a brief evidence matrix for the stated requirements: 8+ years in engineering, 4+ in ML/AI platforms or LLM applications, production RAG, Databricks on Azure or a comparable platform, Python APIs, and regulated-industry governance. Note where experience is directly relevant rather than implying a match.

• Draw a platform design

  As an interview exercise, sketch a Databricks-on-Azure architecture connecting model serving, a multi-LLM gateway, hybrid retrieval, agent orchestration, Gold-layer data products, and vertical-team APIs. Add a short decision record explaining key tradeoffs.

• Prepare a production-code walkthrough

  Select a relevant project you can discuss and identify the code you owned, API and async design, LLM SDK integration, routing, rate limits, failover, and cost tracking. Be ready to explain operational outcomes and lessons without disclosing confidential material.

• Rehearse a document-workflow design

  Using a hypothetical CIM review or IC memo drafting workflow, outline chunking, embeddings, structured extraction, analytical versus semantic query routing, and schema-constrained agent outputs. Explain how you would test retrieval and generation quality.

• Draft a governance evidence example

  Prepare a hypothetical control diagram showing deal-context separation, Unity Catalog permissions, retrieval and inference access checks, and audit logs. Outline what technical evidence you would bring to an architecture review and governance gate; do not present it as Ares’s existing process.

• Explain release and monitoring decisions

  Outline a sandbox-to-production path with evaluation criteria, staged rollout, rollback, and model or prompt versioning. Show how you would track latency, token spend, retrieval quality, hallucination flags, and per-use-case cost.

• Document cross-team leadership

  Prepare one example of an engineering standard or reusable API adopted across teams, including your code-review and mentoring approach. Relate it to the posting’s hub-and-spoke model without claiming familiarity with Ares’s internal implementation.

• Bring targeted questions

  Ask how the central team prioritizes platform work with vertical teams, where Gold-layer data-product interfaces stand, and what evidence reviewers expect at the AI governance gate. These questions probe responsibilities stated in the posting rather than assuming answers.