AI Engineer

AI Engineer (Top Priority)

Client name: JM Family

Location:

Deerfield, FL (Only local)

Work Type:
Hybrid 3 days onsite per week

Total years: 8-10 years

Key:
Along with strong AI engineer need to have below mentioned pointers.

• The candidate should possess strong stakeholder management skills.
• Excellent communication skills and the ability to articulate ideas effectively are essential.
• The individual should be comfortable working in a challenging and fast-paced environment.
• The role follows a hybrid work model, and the candidate should be local to the client location.
• Building strong rapport with the client is critical; therefore, the candidate is expected to work from the client office on a weekly basis.
• The client is looking to have the selected candidate start within the next two weeks.

Write production code. Repeatable work becomes compiled, testable binaries (Go-first); prompts drive persona behavior only. You build the tooling, not slideware about it.

Operate agent fleets. Take a decomposed backlog, fan out parallel streams on conflict-free work, and bring it back through review gates.

Build security in, not on. TDD test-writers ahead of coders, a dedicated validation stage (models hallucinate passing tests), persona judging against threat/security models, failures auto-looped as issues. Signed builds, SBOMs, and access control are table stakes.

Integrate natively. Azure + Git Hub Copilot, build-what’s-missing in-stack. No third-party platforms, no AWS.

Run model-agnostic. Personas across Claude / Copilot / ChatGPT because they fail differently; disagreement is signal. Manage token and context economics as an engineering constraint — just-in-time context loading, per-task budgets, alerts.

Own your slice end to end. Design, develop, test, integrate, deploy, document, and KT — across the full SDLC.

Must-have

Senior engineer who still ships daily — hands-on-keyboard delivery, no exceptions.

Deep Azure + Git Hub Copilot delivery experience (primary stack, not a footnote).

Strong Go (or equivalent systems language) for deterministic, testable orchestration tooling.

Hands-on experience operating multi-agent / multi-model workflows in real delivery, not demos.

Security-native instincts — TDD discipline, secure SDLC, threat modeling; comfort in a vulnerability-response context.

Full-SDLC ownership of a work stream: design through deployment, docs, and knowledge transfer.

Nice-to-have

SIEM / SOAR / EDR, identity, vuln-management, or CMDB / ITSM integration experience.

Eval-harness, red-team, or agent-guardrail work (blast-radius sandboxing, reversibility, confidence thresholds, audit trails).