Hire Senior AI/ML Engineers
US Timezone Aligned,
100% English Proficient,
Senior AI/ML Engineers.
Building real AI capabilities into your product requires more than running models — it demands engineers who understand the full machine learning lifecycle, from data pipeline design and model training to deployment, monitoring, and iteration at production scale. Coderio gives you immediate access to senior AI/ML engineers, rigorously vetted, nearshore, and ready to add value from day one.
AI/ML Staff Augmentation
★ ★ ★ ★ ★ 4.9 Client Rated
TRUSTED BY THE WORLD’S MOST ICONIC COMPANIES.
AI/ML Staff Augmentation
★ ★ ★ ★ ★ 4.9 Client Rated
AI/ML Staff Augmentation Made Easy.
AI/ML Staff Augmentation Made Easy.
Smooth. Swift. Simple.
Discovery Call
We are eager to learn about your business objectives, understand your tech requirements, and the specific AI/ML engineering expertise your team needs.
Team Assembly
We can assemble your team of experienced, timezone-aligned, expert AI/ML engineers within 7 days.
Onboarding
Our expert AI/ML engineers can quickly onboard, integrate with your team, and add value from the first moment.
About AI/ML Staff Augmentation.
Why Hire AI/ML Engineers Through Coderio.
AI/ML Velocity Without the Hiring Risk
Skip months of recruiting in one of the most competitive talent markets in tech. Our pre-vetted AI/ML engineers are ready to join your team in 7 days, fully aligned with your time zone and workflows.
Senior Depth, Not Junior Guesswork
Every AI/ML engineer in our network has a minimum of 7 years development experience. You get engineers who have designed, trained, and deployed machine learning systems in production, not candidates still experimenting with Kaggle notebooks.
Nearshore, Not Offshore
Our developers operate from our six Latin America development centers, giving you real-time collaboration, overlapping business hours, and zero communication lag with your US-based team.
You Stay in Control
Unlike outsourcing, staff augmentation keeps your AI/ML engineers fully integrated into your team. Following your processes, your tools, and your roadmap, with full visibility into every technical decision.
Enterprise-Tested Standards
Our engineering practices were shaped by Fortune 500 engagements. The same standards applied to Coca-Cola, FedEx, and Santander apply to every developer we place.
Access Specialized Skills
AI/ML Staff Augmentation gives you access to a wide pool of vetted specialists (NLP, computer vision, LLM integration, MLOps, recommendation systems, and more) perfectly matched to your product needs without maintaining a sprawling in-house AI team.
Flexible Staffing Solution
Whether you're building your first ML pipeline, scaling an existing AI product, or integrating large language models into your platform, AI/ML Staff Augmentation provides the flexibility to match your exact needs without long-term hiring commitments.
AI/ML Engineering Across the Full Modern Stack.
AI/ML systems don’t exist in isolation. Our engineers bring deep expertise building and integrating machine learning capabilities with the data infrastructure, backend systems, and cloud platforms your product depends on. Whether your team is building custom models from scratch, fine-tuning foundation models, or integrating third-party AI APIs into your product, our engineers know how to deliver results at production scale.
The AI/ML Tech Stack Our Engineers Master
- Core Languages: Python, R, SQL, Scala
- ML Frameworks: TensorFlow, PyTorch, Keras, Scikit-learn, XGBoost, LightGBM
- LLM & Generative AI: OpenAI API, Anthropic API, LangChain, LlamaIndex, Hugging Face Transformers, RAG architectures, fine-tuning, prompt engineering
- NLP: spaCy, NLTK, Transformers, BERT, GPT variants, text classification, named entity recognition, sentiment analysis
- Computer Vision: OpenCV, YOLO, CNNs, image classification, object detection, segmentation
- MLOps & Model Deployment: MLflow, Weights & Biases, Kubeflow, BentoML, TorchServe, FastAPI, model versioning, A/B testing
- Data Engineering: Apache Spark, Airflow, dbt, Pandas, NumPy, Kafka, ETL pipelines
- Vector Databases: Pinecone, Weaviate, Qdrant, pgvector, Chroma
- Cloud AI Services: AWS SageMaker, Google Vertex AI, Azure ML, GCP BigQuery ML
- Infrastructure: Docker, Kubernetes, Terraform, GPU provisioning
- Data Stores: PostgreSQL, MongoDB, Snowflake, BigQuery, Redshift, S3
- Visualization: Matplotlib, Seaborn, Plotly, Streamlit, Tableau
- Version Control & Collaboration: Git, GitHub, DVC (Data Version Control), Jupyter, VS Code
When Companies Hire AI/ML Engineers Through Coderio.
Building Your First Production ML System
Moving from proof of concept to a production machine learning system is a significant engineering challenge. Our AI/ML engineers design and implement scalable pipelines, model training workflows, and deployment infrastructure that turn experiments into reliable, maintainable product capabilities.
Integrating LLMs and Generative AI Into Your Product
Adding large language model capabilities (chatbots, document intelligence, semantic search, content generation, or AI-assisted workflows) requires engineers who understand both the technology and its practical limitations. Our engineers have production experience with LLM integration, RAG architectures, and prompt engineering across real product environments.
Scaling an Existing AI/ML Platform
Your ML system works, but it's not keeping up with data volume, user demand, or model complexity. We add senior engineers who can optimize pipelines, improve model performance, reduce inference latency, and build the MLOps infrastructure your platform needs to scale.
Building NLP or Computer Vision Capabilities
Specialized AI domains require specialized expertise. Whether you need natural language processing for document analysis, entity extraction, or classification, or computer vision for image recognition, object detection, or video analysis, our engineers bring the depth your project requires.
Establishing MLOps and Model Governance
Many teams build models but struggle with reproducibility, monitoring, and maintaining model quality over time. Our MLOps-experienced engineers implement the tooling, workflows, and governance frameworks that turn ad hoc ML experimentation into a reliable engineering discipline.
Filling a Critical AI/ML Gap
A key AI engineer is transitioning, on leave, or simply unavailable at a moment when your roadmap can't afford to slow down. We provide immediate, qualified coverage to keep your ML initiatives on track.
Reinforcing for a High-Stakes AI Launch
Major AI feature releases, model retraining cycles, or platform migrations often require temporary but elite reinforcement. We provide senior-level AI/ML talent for exactly these moments.
AI/ML FAQs.
- What types of AI/ML engineers does Coderio place?
We place a broad range of AI/ML specialists, including machine learning engineers, data scientists, MLOps engineers, NLP engineers, computer vision engineers, LLM/generative AI engineers, and AI integration specialists. During your discovery call, we identify the specific profile your project requires and match accordingly. - What is the difference between a data scientist and a machine learning engineer?
Data scientists typically focus on exploratory analysis, statistical modeling, experimentation, and deriving insights from data. Machine learning engineers focus on building, deploying, and maintaining production ML systems, writing scalable code, designing data pipelines, and operationalizing models. Many modern AI projects require both profiles, and we can place either or help you determine which fits your current needs. - Can your engineers work with our existing data infrastructure?
Yes. Our AI/ML engineers have extensive experience integrating with a wide range of existing data stacks, including Snowflake, BigQuery, Redshift, PostgreSQL, MongoDB, Kafka, and S3-based data lakes. We work within your current infrastructure rather than requiring you to rebuild around a new stack. - How do your engineers approach LLM and generative AI integration?
Our engineers evaluate each use case individually. Assessing whether off-the-shelf API integration, retrieval-augmented generation, fine-tuning, or a custom model approach is most appropriate given your data, latency requirements, cost constraints, and accuracy needs. We prioritize practical, production-ready solutions over technically complex ones that don’t justify the overhead. - How do your engineers handle model monitoring and drift in production?
Our engineers implement monitoring pipelines that track model performance metrics, data distribution shifts, and prediction quality over time. They establish alerting thresholds, retraining triggers, and evaluation frameworks that ensure your models remain accurate and reliable as your data and user behavior evolve. - What is RAG, and when does my product need it?
RAG, or Retrieval-Augmented Generation, is an architecture that combines a large language model with a retrieval system (typically a vector database) to ground model responses in your specific data rather than relying solely on the model’s training. It is the standard approach for building document Q&A systems, enterprise knowledge bases, and AI assistants that need to reference proprietary or up-to-date information. Our engineers have production experience designing and optimizing RAG pipelines across multiple industries.
Success Cases.
Success Cases.
Helping businesses of all sizes across the Americas flourish.
Helping businesses of all sizes across the Americas flourish.
Only the Best AI/ML Engineers.
Our rigorous vetting process does the hard work of finding the top engineers.
Finding an AI/ML engineer who can deliver in production (not just in notebooks) requires a fundamentally different kind of evaluation. Our selection process combines technical screening, real code and model review, and deep technical interviews conducted by senior engineers, evaluating depth across the full ML lifecycle: data pipeline design, model architecture decisions, training and evaluation methodology, deployment engineering, and production monitoring. We don’t just verify that an engineer knows PyTorch or can describe a transformer architecture; we verify that they can translate a business problem into a well-scoped ML solution, make sound tradeoffs under real constraints, and build systems that hold up over time.
What sets our process apart is the bar we hold on the non-technical side. Working nearshore demands engineers who communicate proactively, adapt to your workflows, and operate as true team members rather than remote contractors. Every AI/ML engineer we place has been evaluated for English fluency, responsiveness, and professional maturity. Because technical depth without collaboration is only half the equation.
Our Superpower.
We build high-performance software engineering teams better than everyone else.
Expert Developers
Our software developers have extensive experience in building modern applications, integrating complex systems, and migrating legacy platforms. They stay up to date with the all the latest tech advancements to ensure your project is a success.
High Speed
We can assemble your software development team within 7 days from the 10k pre-vetted engineers in our community. Our experienced, on-demand, ready talent will significantly accelerate your time to value.
Full Engineering Power
Our Guilds and Chapters ensure a shared knowledge base and systemic cross-pollination of ideas amongst all our engineers. Beyond their specific expertise, the knowledge and experience of the whole engineering team is always available to any individual developer.
Enterprise-level Engineering
Our engineering practices were forged in the highest standards of our many Fortune 500 clients.
Cross-industry Experienced Engineers
Our Engineering team has deep experience in creating custom, scalable solutions and applications across a range of industries.
Commitment to Success
We are big enough to solve your problems but small enough to really care for your success.
Client-Centric Approach
We believe in transparency and close collaboration with our clients. From the initial planning stages through development and deployment, we keep you informed at every step. Your feedback is always welcome, and we ensure that the final product meets your specific business needs.
Custom Development Services
No matter what you want to build, our tailored services provide the expertise to elevate your projects. We customize our approach to meet your needs, ensuring better collaboration and a higher-quality final product.
Extra Governance
Beyond the specific software developers working on your project, our COO, CTO, Subject Matter Expert, and the Service Delivery Manager will also actively participate in adding expertise, oversight, ingenuity, and value.
Hiring AI/ML Engineers Through Coderio FAQs.
How quickly can I get an AI/ML engineer?
In most cases, we can match you with a qualified AI/ML engineer and have them onboarded within 7 days of your discovery call. For highly specialized profiles (such as computer vision or MLOps engineers) we will give you an accurate timeline during the discovery call.
Do I interview the candidates before they join my team?
Yes. You will have the opportunity to meet and evaluate shortlisted candidates before making a final decision. If you choose to skip the interview stage and move directly to onboarding, we can have a pre-vetted AI/ML engineer on your team even faster.
Can I hire more than one AI/ML engineer at a time?
Absolutely. We can assemble a complete AI/ML engineering team or provide individual specialists depending on your needs, scaling up or down as your project demands change.
What happens if the engineers isn't a good fit?
We stand behind our placements. If an engineer isn’t meeting expectations, we will work with you to find a replacement promptly.
Is there a minimum engagement period?
We accommodate both short-term and long-term engagements. Contact us to discuss the arrangement that best fits your situation.
Can I scale my AI/ML team up or down as the project evolves?
Yes. One of the core advantages of staff augmentation is flexibility. You can add AI/ML engineers as your roadmap expands and reduce the team size when a project phase is complete — without the overhead or risk of permanent hiring decisions.
Will my AI/ML engineer work exclusively with my team?
Yes. When you hire an AI/ML engineer through Coderio, that engineer is dedicated exclusively to your team and your project. They integrate into your workflows, attend your standups, and operate as a full member of your organization.
Do your AI/ML engineers sign NDAs and IP agreements?
Yes. All Coderio engineers are covered by confidentiality and intellectual property agreements before beginning any engagement, ensuring your codebase, data, and proprietary information are fully protected from day one.
Book a Discovery Call.
The talent you need is just a call away, ready to become a seamless extension of your team.