01
LLM applications
Copilots, assistants, extraction, classification, and workflows with measured quality.
Staff Augmentation · AI & ML Engineers
Hire AI & Machine Learning Engineer
Applied AI engineers who move from prototype to evaluated, observable production systems across LLMs, RAG, predictive ML, and automation.
- 72 hours
- to a vetted shortlist
- 14-day trial
- replace at no extra fee
- Your tools
- Repos, tickets, standups
- 0+ Years delivering applied ML
- 0+ AI and data specialists
- 0+ Production AI deployments
- 0× Faster from pilot to production
Trusted by ambitious teams
Built with teams that expect more.
A selection of organisations that have trusted Techora to design, build, and scale critical digital products.
What you can hire
AI engineers matched to a real product outcome
We match the engineer to your data, risk tolerance, evaluation problem, and deployment environment - not just a model name.
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RAG systems
Retrieval, chunking, reranking, citations, permissions, and evaluation for trusted answers.
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Predictive machine learning
Forecasting, scoring, recommendation, anomaly detection, and decision support.
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Computer vision
Detection, classification, OCR, and visual inspection designed for production constraints.
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MLOps
Versioned data, experiments, deployment, monitoring, and retraining without notebook lock-in.
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AI platform integration
Secure model gateways, structured outputs, fallbacks, cost controls, and product telemetry.
Why teams hire us
AI that earns trust after the demo
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Evaluation before scale
Quality, latency, cost, and failure cases are measured against a useful baseline.
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Production engineering included
APIs, queues, caching, observability, and access control are part of the AI system.
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Data reality respected
Engineers work with the data you have and make gaps visible before promising accuracy.
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Human review where needed
High-risk decisions get confidence thresholds, escalation, and clear auditability.
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Model choice stays flexible
Open and hosted models are compared on your workload instead of vendor preference.
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Costs remain visible
Token, GPU, storage, and retrieval costs are traced back to product behavior.
Technologies
What our AI engineers use in production
Models
OpenAI, Anthropic, Gemini, Llama, Mistral
ML
PyTorch, TensorFlow, scikit-learn, XGBoost
Retrieval
LangChain, LlamaIndex, pgvector, Pinecone, Weaviate
MLOps
MLflow, Weights & Biases, Airflow, Docker, Kubernetes
How hiring works
Three steps. Then the AI work has a production owner.
You assess the engineer on your use case, data constraints, evaluation approach, and system design.
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01
Define the outcome
We translate the AI idea into data, quality, latency, safety, and deployment requirements.
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02
Technical interview
Review an evaluation plan, RAG design, model trade-off, or production architecture.
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03
Start
The engineer joins the experiment and product backlog within the first week.
Engagement
Flexible engagement. The same senior bar.
AI advisory
Part-time senior support for feasibility, architecture, evaluation, and model decisions.
Dedicated AI engineer
A full-time applied AI engineer embedded with product, data, and platform teams.
AI delivery pod
Applied AI, data engineering, backend, and MLOps coverage for a full product stream.
Once they join
How they work inside your team
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Start with a baseline
Manual, rules-based, and simple model baselines establish whether AI adds value.
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Build the evaluation set
Representative examples and failure cases become a repeatable quality gate.
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Design the system
Retrieval, prompts, models, guardrails, and fallbacks are treated as one product.
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Ship behind controls
Feature flags, review paths, and telemetry reduce risk while real usage grows.
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Observe quality and cost
Drift, latency, failures, and spend are visible at feature and customer level.
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Improve from evidence
Prompts, retrieval, models, and data evolve from measured failures, not guesswork.
Start this week
Tell us the AI outcome, data, and production constraint.
A short use-case brief is enough. We return applied AI and ML profiles within two business days.
- LLM & RAG
- Predictive ML
- MLOps
- Evaluation
- Python & PyTorch
- Production AI