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Machine Learning / AI Engineer Role Overview

We are seeking a Machine Learning / AI Engineer who will be responsible for designing, building, and scaling production-grade machine learning systems with a strong focus on AI and MLOps for Real-Time Location Systems (RTLS). In this role, you will take ownership of the entire delivery process. This includes managing data pipelines, developing models, deploying solutions, monitoring systems, and driving continuous improvement. You will collaborate closely with teams across Hardware, Software, and Product to ensure the successful release of reliable machine learning features that deliver positive business outcomes.

Responsibilities

ML Development: Design, train, and evaluate machine learning models tailored for location detection and other RTLS applications.
Build ML algorithms from the ground up according to business requirements, or fine-tune existing models—including those from open-source sources or model repositories such as Hugging Face.
Own the full ML lifecycle: Manage all stages including problem framing, data exploration, feature engineering, model training, evaluation, and integration into products.
AI / LLM / RAG– To build, deploy and maintain LLM models which can retrieve data in Natural language from a large structured or unstructured data sources using RAG / Vector DB.
Gather data from diverse sources, ensuring the accuracy and completeness of all collected information.
Utilize statistical methods and machine learning techniques to analyze datasets, reveal patterns, and generate actionable insights.
Create clear and compelling visualizations to communicate findings effectively to both technical and non-technical stakeholders.
Work closely with cross-functional teams to understand data needs and deliver tailored solutions.

Requirements

Minimum of 3 years of experience as a machine learning or data engineer, or a master's degree in Computer Science, Statistics, Mathematics, Data Science, or a related field with at least 1 year of relevant work experience.
Proficiency in Python, with working knowledge of additional programming languages such as C# and Rust considered a plus.
Expertise in machine learning frameworks including PyTorch, TensorFlow, and AWS SageMaker Studio.
Experience with MLOps tools such as MLflow, Kubeflow, Tecton/Feast (feature stores).
Experience with deploying and hosting LLM on bare metal GPU / HW.
Expertise in cost optimization for hosting LLM model on prem or on cloud.
Experience building and operating REST or gRPC services.
Experience with data manipulation and analysis tools, including SQL and data visualization libraries.
Solid understanding of statistical methods and machine learning algorithms.
Strong verbal and written communication skills, capable of conveying complex information clearly.
Excellent analytical and problem-solving abilities.
Ability to work effectively as part of a collaborative team environment.
Demonstrated capability to work independently.

Preferred Qualifications

Cloud: Proficiency with platforms such as AWS SageMaker, Azure ML, or GCP Vertex AI.
Experience in hosting and consuming Hugging Face models.
Infra: Familiarity with Docker, Kubernetes, and CI/CD tools such as GitHub Actions, GitLab, or Azure DevOps.
Data visualization expertise with tools such as Quicksight or Power BI.
Building and deploying complex Agentic AI and LLM

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

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