AI & data engineers who ship models to production.
Hire ML engineers, data engineers, MLOps specialists, and AI architects from a company that has shipped 8+ AI-led products. Engineers who embed in your team and deliver production-grade AI from week one.
AI engineers who ship, not just experiment.
8+ AI products in production. ML, data, MLOps, and GenAI engineers embedded in your team.
Production AI, Not Science Projects
Our engineers have shipped 8+ AI products to production. They build models with monitoring, retraining, and drift detection — not just Jupyter notebooks that never leave the lab.
Full-Stack Data Engineering
From ingestion to insight — our data engineers build the pipelines, warehouses, and feature stores that feed your ML models. No gaps between data and intelligence.
Your Team, Your Workflow
We embed into your existing stack — Databricks, Snowflake, SageMaker, Vertex AI. Your tools, your ceremonies, your standards. Indistinguishable from in-house hires.
ML Engineering
PyTorch, TensorFlow, scikit-learn — custom models for classification, regression, recommendation, and anomaly detection trained on your domain data.
Data Engineering
Snowflake, BigQuery, Databricks, dbt — scalable data pipelines, warehouses, and lakehouses that make your data ML-ready.
MLOps & Deployment
MLflow, SageMaker, Vertex AI — model versioning, A/B serving, drift detection, and automated retraining pipelines for production ML.
LLM & GenAI
Fine-tuning, RAG pipelines, prompt engineering, and AI agent development on OpenAI, Gemini, Claude, and open-source models.
Computer Vision
Object detection, image classification, video analytics, and OCR — production vision systems for manufacturing, healthcare, and security.
Analytics & BI
Tableau, Power BI, Looker — executive dashboards, predictive analytics, and self-service analytics platforms built on solid data foundations.
Five stages, paired end-to-end.
Predictable delivery. No black-box sprints.
Scope
Define your AI/data requirements, team structure, and engineering culture expectations.
Match
We shortlist vetted AI & data engineers within 48 hours — live coding assessments, ML system design, and domain fit.
Embed
Engineers join your daily standups, use your tools, and follow your development practices from day one.
Ship
Production models with monitoring, versioning, and automated retraining — not just prototypes.
Scale
Add or rotate engineers as your AI roadmap evolves. No long-term lock-in.
Why enterprises trust us with their AI.
Real outcomes our clients report within the first engagement cycle.
Higher automation rates
Eliminate repetitive tasks and free your team to focus on strategic work.
Measurable accuracy
AI models with tracked precision, recall, and F1 scores — not guesswork.
Faster decision-making
Real-time insights and predictions that accelerate business decisions.
Reduced operational cost
Automation that pays for itself within the first quarter.
Production-grade reliability
Guardrails, monitoring, and fallback logic built into every AI system.
Knowledge transfer
Your team learns to maintain and extend AI systems independently.
Tools our ai & data engineers developers ship with.
We use what works. No vendor lock-in.
Three ways to work with our AI & Data Engineers team.
Scale up, scale down — zero procurement headaches.
Fixed-scope project
Start-to-finish delivery with total cost, timeline, and scope agreed upfront. Best for well-defined builds and launches.
Dedicated team
A ring-fenced squad — PM, tech lead, engineers, QA — fully managed by us, embedded in your workflow.
Staff augmentation
Plug senior engineers into your existing team and tools. You manage priorities, we deliver results.
Asked first, every time.
Most teams are staffed within 1–2 weeks. We propose vetted profiles within 48 hours.
ML engineering, data engineering, MLOps, computer vision, NLP/LLM, and GenAI (RAG, AI agents, prompt engineering).
You do. All models, code, pipelines, and documentation belong to you — full stop.
An AI engineer focuses on designing, training, and deploying machine learning models, whereas a data engineer focuses on building the data pipelines and infrastructure required to collect, clean, and store the data used by those models.
Our MLOps engineers utilize containerization (Docker/Kubernetes), automated CI/CD pipelines, and robust monitoring tools to ensure that machine learning models can scale dynamically with user traffic while maintaining high inference speeds.
Yes, we offer flexible engagement models. You can hire ML engineers on a project basis to solve specific challenges, or opt for dedicated AI staffing for long-term product development.
Absolutely. We specialize in providing dedicated AI staffing, seamlessly integrating our top-tier AI and data engineers into your internal teams for continuous, long-term enterprise development.
Our data engineers are highly proficient in modern data stacks including Apache Spark, Kafka, Snowflake, Databricks, AWS Redshift, and Google BigQuery, ensuring robust ETL pipelines.
We enforce a rigorous vetting process that includes advanced algorithm assessments, real-world system design interviews, and hands-on ML model deployment tasks before onboarding any AI engineer.
Yes, we specialize in legacy modernization. Our data engineers can architect and execute a secure migration from legacy on-premise databases to scalable cloud data lakes without data loss.
Get AI engineers on your team this week.
Tell us about your project. We'll come back with a plan, a timeline, and the right team — no obligations.