Hire AI engineers to work alongside your internal team.
Our engineers embed directly into your existing workflow, with access to your codebase, data infrastructure, and MLOps pipeline from day one.
Expert minds behind every project
Seeking to hire artificial intelligence engineers?
Building LLM-powered features, automating workflows, scaling intelligent systems – none of them have room for on-the-job learning.
Get engineers who’ve already paid that tuition, production-hardened, technically deep, ready to contribute from day one.
Alexey Karankevich
AI Innovation Lead,
15+ years in ML
AI Innovation Lead,
15+ years in ML
Yariv Z Levy
AI Strategy Advisor,
PhD in AI, MSc
AI Strategy Advisor,
PhD in AI, MSc
Smarter AI with veteran talent
Tap into AI software development services delivered by a seasoned team that owns every step.
Senior specialists→
Nearly 80% of the team are senior-level engineers, averaging 10 years of hands-on engineering experience. They support complex architectures, performance-critical models, and stable pipelines without constant oversight or rework.
PhD-level experts→
Published researchers on the team and engineers with academic lineage tracing back to Turing Award-winning labs. This isn’t experimental AI, they apply deep understanding of models and algorithms to real, production-grade work.
Product ownership→
Our engineers build and scale AI products from the ground up and bring that same ownership to client work. 350+ projects delivered, with full accountability across architecture, modeling, and iteration.
Roles for every AI bottleneck
Common request
We’re building on top of LLMs but the system keeps breaking in unpredictable ways, like hallucinations, tool-calling failures, agents that loop or stall. We need engineers who understand how these systems actually fail, not just how to wire them together.
What you walk away with
- Agents that complete multi-step tasks reliably under real load and edge cases
- RAG pipelines that retrieve what’s relevant, not just what’s semantically close
- Tool-calling architectures that fail gracefully instead of silently
- LLM behavior that stays within defined boundaries across sessions and user types
- A system you can audit, trace, and debug, not a black box you’re afraid to touch
Research depth, production track record
Production deployments and agentic AI workshops, built and run by the same team. The shortest path to a working system is a conversation with someone already inside one.
Engagement models
AugmentationStaff augmentation
Project-basedProject-based hiring
We take a feature from spec to production: model training, integration, testing, and deployment.
Handoff includes documented architecture, reproducible training pipelines, and code your engineers can own without a knowledge transfer marathon.
Dedicated teamDedicated team
A persistent AI squad aligned to your product roadmap for the long haul.
They cover the full AI development lifecycle: experimentation, pipeline scaling, model governance, and iteration.
Where does an AI squad move the needle for your project?
Put the specifics in, like scope, team size, use case, and get a grounded read on where the investment holds up and where it doesn’t.
Industry expertise
Video and streaming
With 20+ years in online video, our AI engineers build recommendation engines, adaptive pipelines, and moderation tools for millions of viewers.
AdTech
Nearly 20 years in AdTech power optimization of DSPs, SSPs, and retail media platforms, turning fast-moving data into better targeting and campaign ROI.
EdTech
Oxagile’s engineers develop personalized learning platforms and adaptive assessments, using AI, data-driven personalization, and scalable delivery.
Transportation
Our experts design predictive routing, demand forecasting, and fleet optimization tools using real-time IoT and logistics data.
Finance
Specialists in fraud detection, risk scoring, and automated trading systems, providing real-time insights for high-volume, compliant financial operations.
Healthcare
We deliver telemedicine and patient monitoring platforms that convert real-time device and EHR data into secure, actionable insights.
Products our engineers own, start to finish
Austra Nova and Voiager are Oxagile products, taken from spec to production by the same engineers who join client work. Proof the talent ships, not just prototypes.
Austra Nova
A full calling operation that places and answers calls, qualifies leads live, and updates connected systems on its own. Built and run in production by the team.
Voiager
An AI voice assistant that runs interviews and captures insights at enterprise scale, engineered, deployed, and maintained in-house.
Our AI development stack
LLM platforms
OpenAI GPT, GPT-5 • Anthropic Claude family • Google Gemini family • AWS Titan models • IBM Granite models
Open foundation
Meta Llama 3.x family • Mistral and Mixtral series • Qwen family • DeepSeek V-series and R-series • Microsoft Phi-3 family • Stability AI series
Specialized models
Long-context transformers (xLAMs) • Multimodal LLMs • Code-optimized models (e.g., Code Llama, StarCoder 2)
Deep learning
PyTorch • Keras • scikit-learn • JAX • Hugging Face Transformers and Diffusers • PyTorch Lightning • JAX Flax • OpenAI and Anthropic toolchains • ONNX
RAG & search
LangChain • LlamaIndex • Nvidia NeMo • Vector Database • Sentence Transformers • Cohere Embed • Chunking, ranking, and hybrid search orchestration
Agent frameworks
Multi-agent orchestration • Tool-using and function-calling agents • Planning and reasoning support • Memory-augmented workflows
ML competencies
Supervised, unsupervised, self-supervised • Contrastive • Clustering and metric learning • RL/RLHF • Few and zero-shot tuning • RAG and tool use • Multimodal and cross-modal
FAQ
The process usually starts with defining the problem you want to solve, such as building a model, integrating AI into an existing product, or taking a prototype into production. From there, we offer candidates with hands-on experience in relevant languages and ML frameworks, a track record of shipping production code, and familiarity with deployment and maintenance.
LLM systems, RAG pipelines, tool-calling, agentic orchestration, alongside computer vision, NLP, MLOps and ML infrastructure, and classical ML, also recommendation engines, anomaly detection, forecasting. If it runs in production, we’ve likely built something close to it.
Staff augmentation drops individual engineers into your existing structure, they work inside your team, under your processes. A dedicated team is a self-contained squad of 3–7 people with its own technical lead, reporting cadence, and full accountability for delivery. The first extends your capacity. The second replaces the need to build that capacity yourself.
Those planning to hire AI teams or individual AI developers look for those who know Python, R, Java, or C++, and frameworks like PyTorch or scikit-learn. Experience working with data processing, feature engineering, model training, deployment, and MLOps principles matters too.
Dedicated AI engineers focus on implementing and deploying models in production, writing scalable code, and maintaining systems. Data scientists focus on analyzing data, exploring patterns, and creating experimental models.
AI engineers are in demand across industries like finance, healthcare, AdTech, video and streaming, transportation, and education, often working on predictive analytics, recommendation engines, automation, or intelligent agents.
