AI and machine learning engineers
Build model-assisted product features, eval loops, data paths, and production monitoring instead of prototype-only demos.
Hire AI engineers who have already shipped to production
Hire senior AI engineers, LLM engineers, and dedicated AI teams who embed in your repo, work with your technical leads, and own production outcomes.
Client proofTeams that shipped with CoderPush.
Roles
Hire the AI engineers your product actually needs.
Generative AI and LLM engineers
Ship RAG, agent workflows, tool calling, prompt systems, and human review flows inside real products.
MLOps and AI platform engineers
Own deployment, observability, latency, cost, access control, and rollback paths for production AI systems.
AI data engineers
Prepare semantic layers, pipelines, permissions, retrieval stores, and governed data access for AI features.
RAG and agentic engineers
Design multi-step workflows with evals, traces, fallback behavior, and clear operational ownership.
AI product full-stack engineers
Build the customer-facing screens, review tools, admin surfaces, and dashboards that make AI usable.
Your next milestone
What do you need to ship next?
An AI feature people can use
Product interfaces for RAG and agent workflows, with evaluation and human review.
AI connected to your data
Retrieval, data pipelines, permissions, and integrations with your existing services.
Production foundations for AI
Deployment, monitoring, model routing, cost controls, and rollback planning.
Compare
Choose for shipped output, not the lowest hourly rate.
CoderPush Vietnam
Large offshore vendor
High-cost contractor
Senior rate
CoderPush VietnamMid
Large offshore vendorLow
High-cost contractorHigh
Minimum engagement
CoderPush Vietnam6 weeks
Large offshore vendor3 months
High-cost contractor3 months
Talk to senior engineer on day 1
CoderPush VietnamYes
Large offshore vendorOften no
High-cost contractorSometimes
Timezone overlap with US teams
CoderPush VietnamPlanned overlap and handoff
Large offshore vendorLimited
High-cost contractorStrong
Production AI ownership
CoderPush VietnamCore fit
Large offshore vendorVaries
High-cost contractorVaries
Process
How an embedded AI engineer starts producing signal.
Days 1-2
Shape the role
Clarify the product surface, model and data stack, security constraints, timezone needs, and seniority bar.
Days 3-5
Meet matched engineers
Review senior profiles matched to your delivery risk: AI product, backend, data, cloud, or full-stack ownership.
Week 1
Start inside your repo
Onboard to your rituals, read the architecture, map risks, and start with a useful pull request.
Week 3+
Scale only when useful
Add capacity after the first operating loop is working, not because a staffing plan says the team must grow.
Case Study Samples
Proof from work closer to production than a demo.
SaaS platformSleek
Digital corporate secretary platform
Product engineering for a startup operations platform where legal workflows, document handling, and scale mattered from the beginning.
Read case studySoftware teamAxon ActiveProject management platform delivery
Delivery collaboration for a software organization where product workflow, engineering discipline, and team scalability were the core problem.
Read case study+
AWS Advanced Tier Services Partner and GenAI delivery signal.
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60+ clients worldwide and 80+ senior engineers across product, data, and cloud.
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Public international product and platform case studies available for buyer review.
Engagement
What has to be clear before an AI engineer joins.
Engagement model
Start with one senior engineer or a small dedicated AI team embedded in your repo, rituals, and review process.
Pricing clarity
We scope the role, seniority, and sprint rhythm before quoting. The first checkpoint is designed to validate fit early.
Security and IP
NDA, IP assignment, least-privilege access, code review, logging, and data handling expectations are set before repo access.
US buyer de-risking
English communication, planned timezone overlap, written handoff, and senior escalation keep offshore delivery visible.
FAQ
Common questions before extending a team.
01Is CoderPush a staffing vendor?+
No. We can extend your team, but the stronger fit is when senior engineers need to own product judgment, architecture, AI integration, and delivery outcomes.
02How fast can an AI engineer start?+
For the right fit, the match conversation can happen within a few days. Real start timing depends on role scope, security onboarding, and engineer availability.
03Can engineers work inside our repo and rituals?+
Yes. The normal model is embedded delivery: your repo, your standups, your review process, and clear senior ownership from CoderPush.
04Do you provide AI engineer staff augmentation or dedicated AI teams?+
Both. We can place senior AI engineers into your team or assemble a dedicated AI team when the problem needs product, data, cloud, and full-stack ownership together.
05Can we hire AI engineers in Vietnam for US timezone work?+
Yes, with planned overlap and written handoff. We do not pretend every hour overlaps; we design the operating rhythm so work remains visible and reviewable.
06What roles can we hire?+
Common roles include AI and machine learning engineers, LLM engineers, MLOps engineers, AI data engineers, RAG engineers, agentic engineers, and AI product full-stack engineers.
07How do pricing and minimum engagement work?+
Pricing depends on seniority, role mix, scope, and commitment. The current hiring lane is designed around a focused minimum engagement rather than open-ended staffing volume.
08What if we need a scoped AI product instead?+
Then start with the AI product lane. We can recommend a scoped build, embedded team, audit, or no-build advice if the problem is not ready.
Next step
Need an expert view before you hire?
Bring the role, roadmap, repo context, and risks. We will help decide whether you need one AI engineer, a dedicated AI team, a scoped sprint, or an audit first.