Adjacent Roles
The five core roles attract the most applicants because they have the most recognizable titles. These three do not — and that is exactly why they are worth considering. Each owns a problem that organizations discover after they deploy agents, which means the demand arrives later but the candidate pool is thinner and the hiring bar is often more about demonstrated competence than about pedigree. All three also convert cleanly into a core role once you are inside, so none of them is a dead end.
AI Incident Response Analyst
The problem it owns: an agent did something wrong, and someone has to reconstruct what happened. Not “the model hallucinated” — a specific chain: which input triggered it, which tool call executed, what data left the boundary, whether the state that caused it persists in memory, and whether the same trigger still works.
This is digital forensics with an evidence base that traditional IR training never covered. Your artifacts are prompt and completion logs, tool invocation traces, retrieval hit lists, memory writes, and token-level timing — not disk images and packet captures. Determinism is gone, so “reproduce the incident” becomes “establish a reproduction rate”, and your timeline reconstruction has to survive the fact that the same input may not produce the same output twice.
Why it is emerging now: organizations deployed agents before they instrumented them. The first serious agent incident forces the realization that the logs needed to answer “what did it do and why” were never collected. Whoever can answer the question with the logs that do exist becomes indispensable immediately.
| Aspect | Detail |
|---|---|
| Draws on | SOC and DFIR fundamentals, log analysis, MITRE ATLAS, timeline reconstruction, statistics for non-deterministic behavior |
| Good first project | A post-mortem of an incident you cause yourself: jailbreak an agent you built, then reconstruct the full chain from logs alone — and list every question your logging could not answer |
| Converts into | Agentic AI Red Teamer, or Agentic AI Security Engineer |
| Hardest part | Writing conclusions that are honest about uncertainty without being useless |
The “questions my logs could not answer” section is what makes this project hire-worthy. It shows you understand that IR readiness is a logging design problem, and it hands the reader a concrete instrumentation gap list. Pair it with incident patterns and Lab 4.
What the work adds to a conventional IR skillset, concretely:
| Traditional IR artifact | Agentic equivalent | New difficulty |
|---|---|---|
| Process execution log | Tool invocation trace | Arguments are model-generated and often unlogged |
| Network flow | Retrieval hit list | The “connection” is semantic similarity, not an address |
| Persistence mechanism | Poisoned memory or corpus entry | Persists in data an agent trusts, not in a filesystem |
| Root cause | Trigger plus context state | May not reproduce; you report a rate, not a fact |
MLSecOps Engineer
The problem it owns: the pipeline that produces and deploys the model, rather than the model’s behavior at runtime. Where did these weights come from? Who can push to the model registry? Is the fine-tuning dataset the one we think it is? What is inside that quantized checkpoint someone pulled from a public hub, and did anything execute when it was loaded? Which packages, adapters, and prompt templates ship alongside it?
This is supply chain security applied to an artifact type that most supply chain tooling was not built for. Model files are large binaries with serialization formats that have historically permitted code execution. Datasets are mutable. A “model version” in practice is a bundle of weights, tokenizer, config, adapter, system prompt, and tool schema — and most organizations version exactly one of those.
Why it is emerging now: teams pull models and adapters from public hubs at a rate that would horrify anyone who lived through dependency confusion attacks, and the standard software supply chain answers — signing, attestation, SBOM, provenance — are only now being adapted to AI artifacts. Meanwhile ISO/IEC 42001 and EU AI Act obligations are creating hard requirements for exactly this traceability.
| Aspect | Detail |
|---|---|
| Draws on | CI/CD security, artifact signing and attestation, SBOM tooling, container security, registry access control, Terraform |
| Good first project | A signed, provenance-tracked deployment of a locally served model (Ollama or vLLM): SBOM for the full bundle, signature verification gate in the pipeline, and a written account of what an unsigned artifact could have done |
| Converts into | Agentic Security Orchestration Engineer, or AI Security Governance Engineer |
| Hardest part | Defining what “the artifact” even is — bundle scope is the real design decision |
Strong candidates in this role show a pipeline that fails closed. Anyone can generate an SBOM; showing the build rejecting an unsigned or drifted artifact, with the log output to prove it, is the differentiator.
The bundle problem is worth spelling out, because it is the part interviewers probe:
| Component | Typically versioned? | What an attacker gains by changing it |
|---|---|---|
| Weights / checkpoint | Sometimes | Full behavioral control; possible code execution on load |
| Adapter or fine-tune | Rarely | Targeted behavior change that survives evaluation of the base model |
| Tokenizer and config | Almost never | Subtle input handling changes that break assumptions upstream |
| System prompt | Rarely, and usually outside version control | The cheapest and least monitored path to changing what the system does |
| Tool schema | Rarely | New capability granted to an agent with no code review |
Naming that gap and closing one row of it is a complete first project.
AI Privacy Engineer
The problem it owns: agents touch personal and sensitive data constantly, in places privacy programs did not anticipate. Prompts contain customer records. Retrieval corpora contain documents whose access controls were assumed rather than enforced. Memory persists things a user said once and expected to be forgotten. Traces and observability pipelines duplicate all of it into a third system with different retention rules and a broader access list.
The engineering work is least privilege applied to context: what the agent is allowed to retrieve for this user, what gets masked before it reaches the model, what is retained after the session ends, and how a deletion request propagates into memory stores, vector indexes, and log archives that were never designed for deletion.
Why it is emerging now: the observability answer and the privacy answer are in direct conflict. Security wants full prompt and completion logging for investigation; privacy law wants minimization and deletability. Someone has to design the middle ground — redaction at the right layer, retention tiers, and access controls on the trace store — and very few people currently do that work well.
| Aspect | Detail |
|---|---|
| Draws on | Data protection principles, tokenization and masking, access control design, retention architecture, OpenTelemetry-style pipelines |
| Good first project | A retrieval pipeline with per-user document filtering, field-level masking before the model call, tiered retention on the trace store, and a working deletion path across all three |
| Converts into | Agentic AI Security Architect, or AI Security Governance Engineer |
| Hardest part | Making deletion actually work in a vector index and an append-only log |
The deletion path is the credibility test. Masking is easy to demo. Showing that a delete request removes a record from the primary store, the embedding index, the agent’s memory, and the trace archive — and documenting which of those you could not fully solve — is what an interviewer remembers.
The conflict this role exists to resolve, stated plainly:
| Requirement | Security position | Privacy position | Workable middle |
|---|---|---|---|
| Prompt and completion logging | Log everything, indefinitely | Log nothing identifiable | Redact at capture, tier retention by sensitivity |
| Retrieval scope | Broad corpus for better answers | Only what this user may see | Per-user filtering enforced before retrieval, not after |
| Agent memory | Persist context for continuity | Forget on request | Explicit memory schema with a delete path |
| Trace store access | Wide, for investigation | Narrow, minimized | Separate access tier with break-glass and its own audit log |
A candidate who can argue both sides of one of these rows credibly is doing the job in the interview.
Comparing the three
| Role | Timing of demand | Easiest background to enter from | Core role it converts into | Main risk |
|---|---|---|---|---|
| AI Incident Response Analyst | Immediately after first incident | SOC analyst, DFIR | Red Teamer / Security Engineer | Organization may have no logs to work with |
| MLSecOps Engineer | When model sourcing gets audited | Platform, DevSecOps, SRE | Orchestration / Governance Engineer | Being read as generic DevSecOps |
| AI Privacy Engineer | When regulation or a DSAR bites | Privacy, data engineering, GRC | Architect / Governance Engineer | Drifting into pure policy work with no code |
All three share a structural feature worth noticing: each is defined by an artifact class rather than by a phase of the lifecycle. Incident response owns traces, MLSecOps owns build artifacts, privacy engineering owns data in context. That makes each one easy to scope into a first project and easy to explain in an interview — a meaningful advantage over broader core roles where “what exactly did you do” is harder to answer crisply.
Why the adjacent path is often faster
Three practical reasons. First, competition: a posting for AI Red Team Engineer draws a flood of applicants, while MLSecOps draws people who mostly do not know what the acronym covers. Second, transferability: each of these roles is roughly seventy percent an existing discipline plus thirty percent AI-specific knowledge, which is a far shorter ramp than starting fresh. Third, internal mobility: these roles are frequently created inside an organization before they are advertised, so proposing the work where you already are can be more effective than applying elsewhere.
The internal route deserves emphasis because it is underused. If your employer is deploying agents — and most are — the work described on this page already needs doing and probably has no owner. A two-page memo identifying a specific gap (no forensic logging on the agent platform; no signature verification on pulled models; no deletion path through the trace store), with a proposed first step you can execute, is a proposal for a role rather than an application for one. It also generates a portfolio artifact whether or not it is approved.
What none of these roles are
To keep expectations calibrated:
- None of them requires training or fine-tuning models. Consuming and constraining models is the job.
- None of them is a research position. These are operational roles that produce pipelines, reports, and controls.
- None of them is a permanent destination unless you want it to be — each exists partly because the core roles have not yet absorbed the responsibility, and the conversion paths in the table above are real.
Picking one
Match on the discipline you already have, not on the one that sounds most interesting. The whole advantage of the adjacent path is the seventy percent you do not have to learn, and choosing against your background gives that advantage away.
| You currently spend your days on… | Start here |
|---|---|
| Alert triage, investigations, timelines | AI Incident Response Analyst |
| Pipelines, builds, registries, infrastructure | MLSecOps Engineer |
| Data handling, access reviews, regulatory obligations | AI Privacy Engineer |
Then produce the one project described above, publish it with an honest account of what it does not solve, and use it as the basis for either an external application or an internal proposal. That single artifact is usually enough to be taken seriously for these roles — which is not true of the more crowded core roles, where the same artifact would be table stakes rather than a differentiator.