01
Forward-Deployed Engineer
Senior Forward-Deployed Engineers (FDEs) who ship AI into your existing systems.
Pre-Vetted Senior AI Engineers, In 48 Hours
Hire AI Engineers Across 40 Specialist Roles
From Forward-Deployed Engineers to LLM Engineers to AI Security to Digital Twin. pre-vetted, 5–10+ years, transparent USD monthly rates, onboard in 48 hours.
48-Hour Onboarding
Pre-vetted seniors
Transparent USD Pricing
No recruitment fee
AI-Native Delivery
Friday demos, weekly
Trusted by CTOs, Engineering Leaders & Operators Worldwide
10+ Years in Business
500+ Projects Delivered
200+ Global Clients
4.9/5 Client Satisfaction
Core AI Engineering
Engineers who build, train, and ship AI systems. from research to production.
02
Machine Learning Engineer
Senior ML Engineers from Devlyn.
03
LLM Engineer
Pre-vetted LLM engineers who build copilots, RAG systems, agents, and tool-calling pipelines.
04
AI Application Engineer
Senior full-stack engineers fluent in modern AI APIs.
05
AI Product Engineer
Senior AI Product Engineers who own AI features end-to-end.
06
Full-Stack AI Engineer
Senior full-stack engineers who own AI features end-to-end.
07
AI Research Engineer
Senior AI Research Engineers who reproduce, adapt, and harden frontier research for your product.
08
NLP Engineer
Senior NLP Engineers who design text classification, extraction, search, and dialog systems with both classical NLP and modern LLMs.
Agents, Retrieval, and Context
The specialists behind real RAG, agents, and AI reasoning systems.
01
Agentic Workflow Engineer
Senior Agentic Workflow Engineers who design multi-agent systems with proper memory, tools, and human-in-loop.
02
Retrieval Engineer
Senior Retrieval Engineers who design embeddings, chunking, hybrid search, and reranking for enterprise RAG.
03
Knowledge Engineer
Senior Knowledge Engineers who build ontologies, taxonomies, and knowledge graphs that AI agents can reason on.
04
Context Engineer
Senior Context Engineers who design memory, retrieval, and tool context for LLM and agent systems.
05
Prompt Engineer
Senior Prompt Engineers who design, evaluate, and version prompts and prompt libraries.
06
AI Systems Engineer
Senior AI Systems Engineers who connect models, data, workflows, and tools into reliable enterprise AI systems.
07
AI Platform Engineer
Senior AI Platform Engineers who build reusable AI tooling, model gateways, evaluation infrastructure, and governance for your whole organization.
08
AI Integration Engineer
Senior AI Integration Engineers who connect LLMs and agents to Salesforce, SAP, NetSuite, Workday, HubSpot, ServiceNow, and bespoke ERPs.
AI Operations & Reliability
MLOps, infrastructure, observability, and SRE for production AI.
01
MLOps Engineer
Pre-vetted senior MLOps engineers who build training pipelines, model registries, deployment automation, drift detection, and CI/CD for AI systems.
02
AI Infrastructure Engineer
Senior AI Infrastructure Engineers who design GPU clusters, optimize inference, and scale distributed compute.
03
AI Reliability Engineer
Senior AI SREs who monitor hallucinations, drift, latency, and cost, and respond when probability bites.
04
AI Automation Engineer
Senior AI Automation Engineers who replace manual ops work with AI-native workflows.
05
Platform Engineer
Senior Platform Engineers who build internal developer platforms with golden paths, scorecards, and AI-aware tooling.
06
Site Reliability Engineer
Senior SREs fluent in distributed systems, SLOs, incident response, and AI-aware observability.
07
Cloud Engineer
Senior cloud engineers fluent in AWS, GCP, Azure, and Kubernetes.
08
Distributed Systems Engineer
Senior Distributed Systems Engineers fluent in concurrency, fault tolerance, and AI-scale data.
Data, Multimodal, and Edge
Data engineering, multimodal AI, edge AI, and digital twin specialists.
01
Data Engineer
Pre-vetted senior data engineers who build streaming and batch pipelines, lakehouses, and feature stores ready for AI.
02
Data Scientist
Senior Data Scientists who own experimentation, predictive modeling, and AI-ready analytics.
03
Analytics Engineer
Senior Analytics Engineers who turn raw data into trusted, AI-ready datasets.
04
Synthetic Data Engineer
Senior Synthetic Data Engineers who generate tabular, text, image, and sensor data for regulated, data-scarce projects.
05
Multimodal Engineer
Senior Multimodal Engineers who combine vision, audio, and text models into real applications.
06
Computer Vision Engineer
Senior Computer Vision Engineers fluent in detection, segmentation, OCR, video understanding, and modern VLMs.
07
AI Edge Engineer
Senior AI Edge Engineers who deploy models to phones, browsers, Jetson, Coral, and embedded hardware.
08
Robotics AI Engineer
Senior Robotics AI Engineers fluent in ROS, NVIDIA Isaac, SLAM, perception, planning, and reinforcement learning.
09
Digital Twin Engineer
Senior Digital Twin Engineers who build predictive simulations of physical systems for manufacturing, energy, logistics, and Industry 4.0.
Security, Governance, and Enterprise
AI security, governance, and architecture for regulated and enterprise environments.
01
AI Security Engineer
Senior AI Security Engineers who threat-model, red-team, and harden LLM systems.
02
AI Governance Engineer
Senior AI Governance Engineers who translate AI policy into engineering controls.
03
DevSecOps Engineer
Senior DevSecOps Engineers who shift security left for AI-heavy pipelines.
04
Enterprise AI Architect
Senior Enterprise AI Architects who design AI strategy, target architecture, governance, and roadmap across the organization.
05
Backend Systems Engineer
Senior backend engineers fluent in Go, Node, Python, Rust, and the AI APIs that ride on top.
06
API Engineer
Senior API Engineers fluent in REST, GraphQL, gRPC, MCP, and OpenAPI.
07
Human-AI Interaction Engineer
Senior Human-AI Interaction Engineers who design AI interfaces, voice flows, and feedback systems.
How to choose the right AI engineer for the outcome you need
A CTO rarely needs “an AI engineer” in the abstract. The real question is which constraint blocks the business outcome: model quality, product integration, retrieval accuracy, workflow automation, infrastructure reliability, governance, cost, data readiness, or user adoption. This section helps buyers map the problem to the right specialist before a shortlist is created.
Decision 01
If the AI feature is not working in the product, start with an AI Application Engineer or Full-Stack AI Engineer
+
When the problem is turning an LLM, retrieval flow, model API, or agent idea into a usable product feature, the right hire needs product engineering depth. They should understand API contracts, auth, latency, streaming UX, fallbacks, telemetry, prompt versioning, eval gates, and how the feature behaves for real users. This is different from pure ML research or infrastructure work. You want someone who can connect model behavior to frontend states, backend services, user permissions, usage limits, analytics events, and support workflows.
Evidence to expect: Expect a working feature path, integration plan, model/provider abstraction, evaluation checklist, UX fallback behavior, and release-ready code review notes.
Decision 02
If answers are inaccurate or hard to trust, look at Retrieval, Knowledge, Context, or Evaluation specialists
+
Bad AI answers are often caused by weak retrieval rather than weak prompting. The right specialist should inspect source quality, document parsing, chunking, metadata, embeddings, hybrid search, reranking, context windows, citation behavior, tool grounding, and evaluation sets. For enterprise systems, they also need to understand permissions, freshness, taxonomy, knowledge graph structure, and how users challenge or correct an answer. The outcome is a system that retrieves the right evidence and exposes uncertainty before the model speaks with confidence.
Evidence to expect: Expect retrieval diagnostics, benchmark queries, failed-answer analysis, source coverage notes, eval datasets, citation rules, and measurable answer-quality targets.
Decision 03
If the pilot works but production feels risky, prioritize MLOps, AI Reliability, Platform, or Security engineers
+
A pilot can look impressive while still being fragile. Production AI needs deployment automation, secrets management, model routing, observability, cost controls, drift checks, incident response, prompt/model versioning, rollback behavior, and security review. If the system touches regulated workflows, customer data, internal tools, or high-volume usage, reliability and governance become core engineering work. The right hire should reduce the operational risk of AI adoption rather than only add more features.
Evidence to expect: Expect monitoring plans, release gates, rollback paths, cost dashboards, data exposure review, incident runbooks, eval automation, and ownership boundaries.
Decision 04
If the business case is unclear, use a Forward-Deployed Engineer or AI Product Engineer first
+
Some AI work fails because the engineering starts before the operating workflow is understood. Forward-Deployed Engineers and AI Product Engineers are useful when you need someone to sit close to users, map the current process, identify high-value automation points, shape the proof of concept, and turn ambiguous requirements into a working workflow. They bridge product, engineering, operations, and leadership so the team does not spend weeks building a clever demo that cannot change a business metric.
Evidence to expect: Expect workflow maps, use-case triage, proof-point definition, integration constraints, adoption risks, and a build sequence tied to measurable operational value.
Shortlisting rules we use before sending profiles
Clarify the constraint
We separate model, data, retrieval, product, infrastructure, security, and adoption problems before matching talent. A weak match happens when every AI problem is treated like the same role.
Match the proof point
The shortlist is built around what should be true by day 7: a working integration, retrieval benchmark, eval plan, workflow map, deployment path, security review, or production-quality handoff.
Check adjacent skills
Most AI roles overlap. We check whether the engineer also needs backend systems, data engineering, product sense, cloud infrastructure, UX judgment, or compliance awareness for your environment.
Avoid resume-only matching
A profile is not enough. We review evidence of shipped AI work, failure handling, security awareness, evaluation discipline, communication quality, and ability to work inside an existing team.
Governance across all AI engineering hires
Every AI engineering engagement starts with scoped access, NDA/IP assignment, role-specific success criteria, and a first proof point the buyer can inspect. For specialist AI roles, governance also covers data handling, model/provider choices, prompt and model versioning, evaluation artifacts, source-code ownership, security review, cost visibility, human review rules, and operational handoff. The goal is not to send the most impressive AI resume. The goal is to put the right senior engineer on the constraint that actually blocks adoption, revenue, reliability, or speed.
Find the Right AI Engineer in 48 Hours
Tell us the stack and the goal. We shortlist pre-vetted seniors within 24 hours and onboard within two business days.
NDA Protected
7-Day Risk-Free Trial
AI-Native Delivery
Same-Day Response
Frequently asked questions
Answers for CTOs, product leaders, and engineering leaders deciding whether to hire one AI specialist, build an AI pod, or start with a discovery sprint.
How do I know which AI engineer role to hire first? +
Start with the constraint, not the title. If the issue is product integration, look at AI Application or Full-Stack AI Engineers. If answers are weak, look at Retrieval, Knowledge, Context, or Evaluation specialists. If the pilot is fragile, look at MLOps, AI Reliability, Platform, or Security. If the workflow is still unclear, start with a Forward-Deployed Engineer or AI Product Engineer.
Can Devlyn help choose between one AI engineer and a pod? +
Yes. We compare the outcome against the roles required to ship it. One specialist is enough when the internal team already owns product, data, infrastructure, and security. A pod is better when the work crosses multiple systems or when speed depends on coordinated product, backend, data, MLOps, and governance work.
What proof should I expect in the first week? +
The proof depends on the role. It may be a working integration, retrieval benchmark, eval set, workflow map, model deployment plan, security review, cost baseline, data-readiness report, or production handoff plan. The point is that the first week should produce evidence you can inspect, not only onboarding updates.
Do AI engineers work inside our existing stack? +
Yes. The engineer joins the repositories, issue tracker, communication rhythm, cloud environment, model providers, data sources, and review process you approve. We scope access before onboarding and define how code, prompts, data artifacts, evaluation results, and documentation are handed back to your team.
How is quality controlled across so many AI roles? +
Quality is controlled through role-specific screening, senior review, scoped proof points, code review, evaluation artifacts, security expectations, and replacement support. AI work is judged against the outcome: reliable answers, usable workflows, maintainable code, safe data handling, controlled cost, and clear ownership.
Can I start small before committing to a longer engagement? +
Yes. Many buyers start with a short discovery or pilot around one proof point. That may be a RAG benchmark, workflow automation slice, model deployment path, AI feature integration, governance checklist, or data-readiness review. If the evidence is strong, the engagement can expand into a dedicated hire or pod.
What if I already have an internal engineering team? +
Devlyn AI engineers can plug into your existing team instead of replacing it. We define where the external specialist owns delivery, where your team reviews architecture, how access and IP are handled, and what artifacts must be handed back. This works well when your engineers know the product but need specialist depth in retrieval, agents, MLOps, AI security, model integration, or data readiness.