Machine Learning Engineering Senior Engineer

Job Title: (Machine Learning Engineering Senior Engineer)


About Kyyba:
Founded in 1998 and headquartered in Farmington Hills, MI, Kyyba has a global presence delivering high-quality resources and top-notch recruiting services, enabling businesses to effectively respond to organizational changes and technological advances.
At Kyyba, the overall well-being of our employees and their families is important to us. We are proud of our work culture which embodies our core values; incorporating value, passion, excellence, empowerment, and happiness, creates a vibrant and productive atmosphere. We empower our employees with the resources, incentives, and flexibility that they need to support a healthy, balanced, and fulfilling career by providing many valuable benefits and a balanced compensation structure combined with career development.


Job Description


Employees in this job function are responsible for designing, building, deploying, and scaling complex self-running ML solutions — including Generative AI and Large Language Model (LLM) systems — in areas such as computer vision, perception, localization, natural language processing, and conversational AI. They automate and optimize the end-to-end ML and Gen AI model lifecycle using expertise in experimental methodologies, statistics, prompt engineering, and coding for tool building and analysis.

Design and develop innovative ML models, Gen AI systems, and software algorithms — including LLM-based architectures (e.g., transformer models, RAG pipelines, fine-tuned foundation models) — to solve complex business problems in both structured and unstructured environments

Skills Required:

GCP, Big Data, Artificial Intelligence & Expert Systems, API 1. GCP – Experience deploying and managing services on Google Cloud Platform, including Compute Engine, Cloud Storage, IAM, and Cloud Functions. For example, designing and implementing a cloud-native application architecture using GKE (Google Kubernetes Engine) with Cloud SQL and Pub/Sub. 2. Big Data – Experience working with large-scale data processing frameworks such as Apache Spark, Dataflow, or Big Query.

For example, building ETL pipelines that process terabytes of daily event data and transform it for downstream analytics.
3. Data Warehousing – Experience designing and maintaining data warehouse solutions (e.g., Big Query, Snowflake, Redshift). For example, modeling a star schema for a retail analytics platform that supports reporting on sales, inventory, and customer behavior.
4. Artificial Intelligence & Expert Systems – Experience developing or integrating AI/ML models and rule-based expert systems. For example, building a classification model using Vertex AI to predict customer churn, or implementing a rule engine that automates underwriting decisions. 5. API – Experience designing, building, and consuming RESTful or gRPC APIs. For example, developing a versioned REST API with OAuth 2.0 authentication that serves as the integration layer between a mobile application and backend microservices.

Skills Preferred:
Google Cloud Platform
1. Google Cloud Platform – Familiarity with advanced GCP services beyond core compute and storage, such as Vertex AI, Dataflow, Cloud Composer (Airflow), and Big Query ML. For example, using Cloud Composer to orchestrate scheduled data pipelines that feed into a Big Query data warehouse.

Experience

Required:

Senior Engineer Exp:
Prac. In 2 coding lang. or adv. Prac. in 1 lang.; guides. 10+ years in IT; 8+ years in development

Experience Preferred:

* Strong understanding of Generative AI principles and architectures, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems.
* Proven experience in building and deploying RAG systems, including the use of
** Vector Databases**.
* Proficiency in Python programming.
* Solid experience with SQL for data manipulation and querying.
* Hands-on experience with Google Cloud Platform (GCP) services relevant to AI/ML.
* Basic understanding and practical experience with Machine Learning model fine-tuning.
* Familiarity with data engineering concepts and practices.
* Expertise in prompt engineering techniques for…