Hire Machine Learning Engineers in 48 Hours !

Uplers ensures a seamless hiring experience by combining AI and human intelligence to vet top-quality Machine Learning Engineers. You receive carefully shortlisted profiles within 48 hours and can onboard the right talent in as little as 2 weeks, helping you hire faster without compromising on quality.

You can receive the top 1% shortlisted profiles within 48 hours through Uplers. Once you finalize the most suitable machine learning engineer, Uplers handles the entire hiring and onboarding process. Depending on your requirements and decision-making timeline, onboarding typically takes 2-4 weeks.

If the developer doesn’t meet your expectations, we offer a 90-day replacement guarantee for full-time hires and a lifetime replacement for contract roles, at no additional cost. Additionally, you can opt for a 30-day cancellation policy with no extra charges, giving you complete flexibility to make changes as needed.

Yes. machine learning engineer in the Uplers network are evaluated for English proficiency and overall suitability for work environments. Beyond language skills, cultural alignment is also assessed to help ensure smooth integration with your team, enabling productive interactions and long-term success.

The network includes machine learning engineers across a wide range of specializations including classical machine learning, deep learning, NLP and LLM engineering, computer vision, time-series forecasting, recommendation systems, and MLOps. Their expertise spans technologies such as PyTorch, TensorFlow, Scikit-learn, XGBoost, Hugging Face Transformers, OpenCV, YOLO, MLflow, Kubeflow, SageMaker, vector databases, RAG pipelines, and production AI deployment workflows.

Yes. ML engineers in the network support both embedded product engineering roles and project-based AI consulting engagements. They can work within product teams to build and maintain production AI features such as recommendation systems, search ranking, personalization, and fraud detection, or deliver standalone consulting projects including predictive analytics, NLP systems, computer vision solutions, and custom AI model development with defined deliverables and documentation.

Yes. ML engineers in the network are matched based on your preferred time zone and working-hour overlap requirements, with many experienced in collaborating across US, UK, EU, and APAC schedules. This supports real-time coordination for experiment reviews, model evaluation discussions, deployment monitoring, sprint planning, and cross-functional collaboration with data, product, and engineering teams.

Yes. Many ML engineers in the network are experienced in end-to-end machine learning deployment workflows, including training pipelines, model serving, experiment tracking, monitoring, feature stores, and scalable MLOps infrastructure. Their expertise includes tools and platforms such as Airflow, Kubeflow, SageMaker, FastAPI, Ray Serve, MLflow, Weights & Biases, Evidently AI, and cloud-native deployment environments for production AI systems.

Yes. Uplers can help build complete AI and ML teams tailored to your product stage and technical requirements. Team compositions commonly include ML Engineers, Data Engineers, MLOps Engineers, Data Scientists, Backend Engineers, and specialized roles such as LLM Engineers, Prompt Engineers, or RAG Engineers. Talent can be scaled based on your evolving needs, with all team members working under a unified engagement structure to support seamless collaboration and long-term growth.

Yes. Many ML engineers in the network specialize in Generative AI and LLM application development, including fine-tuning open-source models, building RAG pipelines, semantic search systems, AI copilots, and multi-model AI applications. Their expertise includes technologies such as Hugging Face Transformers, LangChain, LlamaIndex, vector databases, embedding models, LoRA/QLoRA fine-tuning, prompt engineering, and scalable LLM deployment workflows across modern AI infrastructure.

Yes. Many ML engineers in the network specialize in computer vision and have experience building image classification systems, object detection pipelines, segmentation models, OCR solutions, video analytics, and visual inspection platforms. Their expertise includes frameworks and tools such as PyTorch, TensorFlow, OpenCV, YOLO, Detectron2, Vision Transformers, SAM, ONNX, TensorFlow Lite, and Core ML for both cloud-based and edge AI deployments across industries like healthcare, manufacturing, retail, automotive, and IoT.