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Production AI, not just prototypes
Engineers who ship retrieval, evaluation, telemetry, and guardrails—pairing with product to get real usage safely.
Hire applied AI engineers ready for production.
LLM and ML specialists who connect data, models, and product safely.
Deeptal AI engineers design retrieval, evaluation, and guardrails so AI features ship with confidence—not just demos.
Hire a top AI engineer nowNo-risk trial. Pay only if satisfied.
Clients rate Deeptal AI teams 4.9 / 5.0 on average.
Pulse surveys after onboarding and milestone readouts.Compensation snapshot
Bench-readyAnnual bands across key markets to plan budgets confidently.
US & Canada
$150k – $235k
Glassdoor Oct 2025, total comp
United Kingdom
£75k – £110k
Glassdoor Oct 2025, total comp
Germany
€70k – €105k
Glassdoor Oct 2025, total comp
The Balkans
€40k – €75k
Glassdoor Oct 2025, total comp
Avg. seniority
9.2 yrs
Model to production
14-21 days
From brief to first shipped slice
Safety & evaluation
Red-team + guardrails
Included in sprint 1
Trusted by product and engineering teams
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Delivery highlights
What you get with Deeptal
Senior talent, clear rituals, and proactive communication from week one.
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Responsible by design
Privacy, safety, and compliance baked into data pipelines, prompts, and model selection.
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Data and evaluation rigor
Robust evaluation harnesses, offline tests, human-in-the-loop workflows, and cost/performance tracking.
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Product-grade integration
API design, caching, observability, and UI collaboration so AI features feel seamless to users.
Coverage map
Where this team drives outcomes
Where AI engineers move the needle
Common engagements we run for product, data, and platform leaders.
- LLM-powered search, summarization, and co-pilots with retrieval augmentation.
- Recommendation systems and personalization with clear evaluation metrics.
- Computer vision or structured ML models shipped with monitoring and retraining plans.
- Governance and safety reviews for regulated domains with documentation and playbooks.
How we cover the AI stack
Engineers with depth in data, models, and product delivery.
- Retrieval and orchestration: vector stores, embeddings, prompt pipelines, agent patterns.
- ML Ops: feature stores, model registries, CI/CD for models, and canary deployments.
- Data: ingestion, labeling, quality checks, and privacy-safe handling.
- Evaluation: offline harnesses, human feedback, and continuous monitoring of drift and cost.
Specialties
Specialist coverage by pod
LLM & orchestration
RAG systemsPrompt + tool orchestrationLatency + cost tuningCaching and safety
ML Ops
Pipelines + feature storesModel registry + CI/CDCanary + shadow deploysMonitoring + drift detection
Data & evaluation
Labeling + QA loopsEval harness + red teamingPrivacy + governanceAnalytics + feedback loops
Product delivery
API contracts + SLAsUX collaborationDocs + playbooksStakeholder comms
Sample talent
Meet ready-to-start specialists
Profiles curated for your stack, time zones, and delivery rituals.
Noah E.
Staff AI Engineer
New York | EST
Python, LangChain, Pinecone, FastAPI
Built RAG copilots for support and sales, added eval harness with human-in-loop review, and cut inference cost by 28% via caching and model selection.
Elena G.
Senior ML Engineer
Madrid | CET
PyTorch, Vertex AI, Airflow, dbt
Delivered personalization models with feature store, CI/CD for models, and monitoring dashboards covering drift, bias, and latency.
Kofi A.
AI Platform Lead
Accra | GMT
Kubernetes, Ray, Feast, OpenAI/Azure
Stood up multi-tenant AI platform with safety guardrails, policy enforcement, and transparent cost controls for product teams.
Hiring playbook
How to hire AI engineers
Applied AI engineers bridge data, models, and product. Evaluate them on delivery habits and safety, not just demos.
Define the user outcomes and safety bar
- Document the tasks, constraints, and risk tolerance. This shapes model choices and evaluation.
- Ask candidates how they balance UX with safety and cost for similar products.
Probe data and evaluation rigor
- Discuss how they handle data quality, labeling, and feedback loops.
- Look for concrete evaluation methods, human-in-loop design, and monitoring plans.
Check production experience
- Review how they shipped and monitored AI features: rollback strategies, observability, and canary releases.
- Great candidates have stories about reducing hallucinations, latency, or cost in production.
Validate security and governance instincts
- Ask about privacy controls, secrets management, and compliance considerations in their past projects.
- Listen for clear documentation, audits, and responsible AI guardrails.
Onboard with clarity
- Share data access patterns, compliance constraints, and success metrics up front.
- Pair them with data and product leads in sprint one to align on evaluation and delivery rituals.
How it works
Engage in three clear steps
1
Talk to a delivery lead
Share your AI use case, data sources, and risk profile. We anchor screening to outcomes, not just model buzzwords.
2
Meet hand-selected talent
Within days you see a short list of AI engineers calibrated to your domain, stack, and governance needs.
Average time to match is under 24 hours once the brief is clear.
3
Start with a no-risk sprint
Kick off with a trial sprint and clear success criteria. Swap or scale the team quickly if the fit is not perfect.
Pay only if satisfied after the initial milestone.
Exceptional talent
How we source the top AI engineers
We continuously screen applied AI and ML specialists so teams mobilize fast without sacrificing quality.
Every engineer is assessed for depth, collaboration, and delivery habits—not just model familiarity.
Thousands apply each month. Only top talent are accepted.
Step 1
Language & collaboration evaluation
Communication, collaboration signals, and product intuition checks to ensure they can lead as well as build.
Step 2
In-depth skill review
Technical assessments and architecture conversations tailored to data, retrieval, evaluation, and safety scenarios.
Step 3
Live screening
Optional: Your team can join
Live exercises to test problem solving, observability instincts, and quality bar under real-time constraints.
Step 4
Test project
Optional: You can provide your own brief
A short-term project to validate delivery habits, communication cadence, and production readiness in your domain.
Step 5
Continued excellence
Ongoing scorecards, engagement reviews, and playbook contributions to stay on the Deeptal bench.
Capabilities
Capabilities of AI engineers
Our AI teams excel in retrieval, evaluation, ML Ops, and governance—shipping safe, useful features quickly.
LLM application design
Prompt pipelines, tool use, retrieval strategies, and caching for reliable LLM-powered workflows.
Retrieval-augmented generation (RAG)
Embedding strategies, vector stores, chunking, and freshness guarantees for accurate responses.
Evaluation and safety
Offline eval harnesses, human feedback loops, red-teaming, and safety guardrails to reduce hallucinations and bias.
ML Ops and platforms
Pipelines, feature stores, model registries, and deployment strategies with observability and rollback.
Data quality and privacy
Data validation, PII handling, access controls, and governance for regulated environments.
Performance and cost control
Latency reduction, autoscaling, token and compute cost tracking with clear budgets.
Experimentation and analytics
A/B testing, user feedback loops, and telemetry that connect AI features to business outcomes.
Security and compliance
Secrets management, auditability, and compliance-minded design for sensitive data and domains.
Trusted by product and data leaders
Find the right AI talent for every project
From LLM app builders to ML platform engineers, Deeptal teams match your stack, rituals, and governance needs.
LLM application engineers
Engineers focused on RAG, orchestration, prompting, and UX integration.
ML platform engineers
Specialists in pipelines, feature stores, model CI/CD, and observability.
Data + ML full-stack
Engineers who handle ingestion, labeling, model training, and service integration end to end.
AI product leads
Staff-level leaders who align product, data, and compliance stakeholders while shipping calmly.
FAQs
How much does it cost to hire an AI engineer?▾
AI salaries trend higher due to demand and specialized skills. Glassdoor data from October 2025 shows median total compensation for AI/ML engineers around $176,000 in the US, £96,000 in the UK, and €90,000 in Germany. We calibrate teams to your risk, data, and budget constraints before kickoff.
How quickly can I meet vetted AI talent?▾
Most clients see calibrated shortlists within 48 hours and can start a trial within 14–21 days once the brief is clear. Regulated industries may add a few days for governance.
How do you vet safety and evaluation experience?▾
We assess portfolios, run applied AI screens, and use test projects focused on retrieval, evaluation, and guardrails. References confirm they’ve shipped safely in production.
Can I hire hourly, part-time, or full-time?▾
Yes. We place AI engineers on hourly, part-time, or full-time engagements depending on your roadmap and budget.
What if the first match is not right?▾
We replace quickly at no additional cost during the trial and continue until you are confident in the match.
Explore services
Explore related Deeptal services
Looking for end-to-end delivery? Browse Deeptal programs across technology, marketing, and consulting.
Hiring guide
How to hire AI engineers
Applied AI hiring needs to balance velocity, safety, and cost.
Use this guide to evaluate candidates who can ship responsibly and measure impact.
Is demand for AI engineers high?
Yes. AI feature work is accelerating, and experienced applied AI engineers remain scarce.
Those who have shipped production systems with evaluation and safety are in the highest demand.
What distinguishes great AI engineers?
Strong data and evaluation instincts—not just prompt tinkering.
Experience with ML Ops, observability, and rollback strategies.
Clear communication about risks, costs, and governance.
Core layers to cover
Data and retrieval: quality, freshness, privacy, and retrieval design.
Models and orchestration: selection, prompting, tools, and caching.
Evaluation and monitoring: offline evals, human feedback, telemetry, and guardrails.
When to choose AI specialists vs. platform generalists
Choose AI specialists for complex retrieval, safety, or model-tuning needs.
Choose platform-aware generalists for lighter-weight integrations where delivery speed matters most.
How to run the process
Define user outcomes, risk tolerance, and available data before interviewing.
Use portfolio/code reviews plus live discussions on evaluation, safety, and cost control.
Pilot with a small slice and clear success metrics to validate collaboration.
Median total compensation (Glassdoor, Oct 2025, USD equivalent)
USA
$176,000
Canada
$125,000
United Kingdom
$96,000
Germany
$90,000
Romania
$55,000
Ukraine
$58,000
India
$22,000
Australia
$140,000
Top AI engineers are in high demand.
Move fast with applied AI talent, transparent reporting, and a trial sprint to prove the fit.