ML & LLM engineers
Retrieval pipelines, prompt systems, fine-tuning, and evaluation harnesses integrated with your CI/CD.
Embedded AI Engineering
Embedded AI specialists who ship under your standards.
Senior ML, LLM, and agentic engineers who join your delivery system, under your architecture, repos, and controls.
Capabilities
Roles we embed
Profiles are matched to your stack and delivery phase, not generic full-stack generalists relabeled as AI.
Agent & orchestration engineers
Tool integration, multi-agent coordination, observability, and human-in-the-loop workflow wiring.
ML platform & LLMOps engineers
Model serving, feature stores, monitoring, and cost governance on your cloud accounts.
Applied research engineers
Feasibility spikes, benchmark design, and prototype-to-production paths for novel perception or reasoning tasks.
Embedded
Your tools, repos, and rituals
Senior
Production AI experience required
Accountable
Outcomes tied to your backlog
Transferable
Knowledge stays when we roll off
Perspective
Capacity without staff-aug theater
Traditional staff augmentation optimizes for headcount and billable hours, not for merged pull requests and production incidents resolved.
InheritX embeds engineers as contributors to your teams: they join standups, adhere to your branching strategy, and document decisions in your wikis. Success is measured by shipped capabilities and reduced bus factor on critical AI components.
Engagements include explicit knowledge-transfer expectations so internal hires or existing staff can assume ownership as capacity scales.
How we engage
Embed onboarding
01
Scope & profile definition
Align on skills, clearance or compliance needs, and the backlog items engineers will own in the first sprint.
02
Environment access
Provision identities, repos, and data sandboxes under your security process, no shadow environments.
03
Pairing period
Initial weeks paired with your tech leads on architecture norms, review standards, and deployment paths.
04
Sustained delivery
Embedded engineers operate as team members with regular performance check-ins against agreed outcomes.
Fit
When embeds fit best
Backlog exceeds internal AI bench strength
Targeted senior embeds on highest-risk components
Hiring cycles too slow for committed roadmap dates
Time-boxed specialist capacity against a committed delivery date
Specialist skill for a bounded phase (e.g., eval framework)
Time-boxed embed with explicit deliverable definition
Upskill internal team through paired delivery
Embed plus rotation of your engineers through AI workstreams
Continue
Alternatives & complements
FAQ
Embedded AI Engineering FAQ
When the KPI, architecture standards, or security path is still undefined. Start with AI Consulting & Architecture, then embed once the backlog and guardrails are clear.
Embeds join your existing teams and rituals. Squads are cross-functional pods that own a capability end-to-end with InheritX delivery leadership.
No. Embeds are scoped to architecture, evaluation, and production outcomes under your standards, with explicit knowledge transfer. Success is shipped capability, not billable headcount.
We match your policy, on-site, hybrid, or remote, with time-zone overlap agreed upfront.
Knowledge transfer is part of the engagement: runbooks, pairing, and ownership of critical AI components stay with your team.
Next step
Map this capability to your mandate.
A focused strategy conversation, constraints, systems, and what production readiness looks like for your organization.