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Freelance Google Machine Learning Engineer Jobs in Michigan

Hands-on experience with Google Cloud Platform (Google Cloud Platform) services relevant to AI/ML. Basic understanding and practical experience with Machine Learning model fine-tuning. Familiarity ...

Machine Learning Engineer

Dearborn, MI

$105.50K - $126.60K/yr

Stefanini is looking for a Machine Learning Engineer (Dearborn, MI) For quick apply, please reach ... Skills RequiredTechnical Communication, Communications, Google Cloud Platform, TensorFlow, Data ...

The Machine Learning Engineer will be an essential member of the Research and Development Team, where we engineer large tailor-made systems to solve complex data-related problems from many domains.

Machine Learning Engineer

Dearborn, MI

$105.20K - $126.30K/yr

Machine Learning Engineer #1054987 * Employees in this job function are responsible for designing ... Google Cloud Platform - Familiarity with advanced GCP services beyond core compute and storage ...

Senior Machine Learning Engineer

Warren, MI · On-site +1

$222.91K - $227.20K/yr

Machine Learning Frameworks, including TensorFlow and PyTorch; Mathematical Reasoning and Probability; Programming in C++ or Python; Experience with Robot Operating System (ROS), OpenCV, or PCL;

Senior Machine Learning Engineer

Detroit, MI · On-site +1

$126K - $180K/yr

As a Senior Machine Learning Engineer within the AI Squad at Canopy and reporting to the Director of AI Engineering, you'll contribute to the development of cutting-edge AI solutions to combat ...

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Freelance Google Machine Learning Engineer information

What are the key skills and qualifications needed to thrive as a Freelance Google Machine Learning Engineer, and why are they important?

To thrive as a Freelance Google Machine Learning Engineer, you need a solid background in computer science, statistics, and machine learning, typically supported by a relevant degree and experience with real-world data projects. Familiarity with Google Cloud Platform (GCP), TensorFlow, and certifications like Google Professional Machine Learning Engineer are commonly required. Strong problem-solving abilities, self-motivation, and effective client communication distinguish top freelancers in this field. These skills and qualifications are crucial for delivering robust machine learning solutions tailored to client needs and efficiently navigating remote, project-based work.

What are some common challenges freelance Google Machine Learning Engineers face when working with clients remotely?

Freelance Google Machine Learning Engineers often encounter challenges such as clearly defining project scopes, aligning on deliverables, and managing expectations, especially when working remotely. Communication can be more complex due to time zone differences and varying levels of technical understanding among clients. Staying updated with Google’s latest ML tools and ensuring secure, efficient data sharing are also important. Building strong documentation and regular progress updates can help foster trust and smooth collaboration.

What does a Freelance Google Machine Learning Engineer do?

A Freelance Google Machine Learning Engineer is a technical specialist who designs, develops, and deploys machine learning models using Google’s tools and platforms, such as TensorFlow and Google Cloud AI services. They work independently or with clients to solve data-driven problems, build predictive models, and automate processes using machine learning techniques. Their responsibilities may include data preprocessing, feature engineering, model training and evaluation, and integrating models into production systems. Freelancers often manage multiple projects and must stay updated on the latest ML advancements and Google technologies.

What is the difference between Freelance Google Machine Learning Engineer vs Freelance Data Scientist?

AspectFreelance Google Machine Learning EngineerFreelance Data Scientist
CredentialsKnowledge of Google Cloud ML tools, programming skills in Python, TensorFlowStatistical expertise, programming in Python/R, data analysis skills
Work EnvironmentCloud platforms, AI/ML projects, collaboration with developersData analysis, reporting, model development, client communication
Industry UsageTech companies, AI startups, cloud service providersFinance, healthcare, marketing, research organizations

While both roles involve working with data and models, a Freelance Google Machine Learning Engineer specializes in deploying ML solutions on Google Cloud, focusing on AI/ML engineering tasks. A Freelance Data Scientist primarily analyzes data, builds statistical models, and provides insights. The roles overlap in skills but differ in focus and tools used.

What are the most commonly searched types of Google Machine Learning Engineer jobs in Michigan? The most popular types of Google Machine Learning Engineer jobs in Michigan are:
What are popular job titles related to Freelance Google Machine Learning Engineer jobs in Michigan? For Freelance Google Machine Learning Engineer jobs in Michigan, the most frequently searched job titles are:
What cities in Michigan are hiring for Freelance Google Machine Learning Engineer jobs? Cities in Michigan with the most Freelance Google Machine Learning Engineer job openings:

Machine Learning Engineer

Stefanini

Dearborn, MI

Other

Posted 15 days ago


Job description


Stefanini Group is hiring!
Stefanini is looking for a Machine Learning Engineer, Dearborn, MI (Onsite)
For quick apply, please reach out Saurabh Kapoor at /
You will be 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
ResponsibilitiesDesign, build, maintain, and optimize scalable ML and Gen AI pipelines, architecture, and infrastructure, including vector databases, embedding stores, and LLM serving layersUse machine learning and statistical modeling techniques such as decision trees, logistic regression, Bayesian analysis, and deep learning methods, alongside prompt engineering, retrieval-augmented generation (RAG), and parameter-efficient fine-tuning (PEFT/LoRA) to develop and evaluate algorithms that improve product/system performance, quality, data management, and accuracy Adapt machine learning and Gen AI capabilities to domains such as virtual reality, augmented reality, object detection, tracking, classification, terrain mapping, intelligent document processing, and AI-powered agent workflowsTrain, fine-tune, and re-train ML models and LLMs as required, including supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and instruction tuningDeploy ML models, LLMs, and AI agents into production; run simulations and evaluations (including LLM evals and red teaming) for algorithm development and test various scenariosAutomate model deployment, training, re-training, and Gen AI pipeline orchestration, leveraging principles of agile methodology, CI/CD/CT, MLOps, and LLMOps - including guardrail integration, prompt versioning, and observability tooling Enable model management for model versioning, traceability, and governance - including responsible AI practices, bias evaluation, hallucination mitigation, and content safety controls - to ensure modularity and consistency across environments for both ML and Gen AI systems
Experience RequiredGoogle Cloud Platform - 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. Big Data - Experience working with large-scale data processing frameworks such as Apache Spark, Dataflow, or BigQuery. For example, building ETL pipelines that process terabytes of daily event data and transform it into downstream analytics. Data Warehousing - Experience designing and maintaining data warehouse solutions (e.g., BigQuery, Snowflake, Redshift). For example, modeling a star schema for a retail analytics platform that supports reporting on sales, inventory, and customer behaviorArtificial 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. 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.
Experience PreferredStrong 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 (Google Cloud Platform) 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 interacting with LLMs. Experience with the OpenAI SDK. Experience developing robust APIs, preferably with FastAPI. Proficiency with **version control systems (e.g., Git). Experience with **containerization technologies (e.g., Docker).Google Cloud Platform - Familiarity with advanced Google Cloud Platform services beyond core compute and storage, such as Vertex AI, Dataflow, Cloud Composer (Airflow), and BigQuery ML. For example, using Cloud Composer to orchestrate scheduled data pipelines that feed into a BigQuery data warehouse.
**Listed salary ranges may vary based on experience, qualifications, and local market. Also, some positions may include bonuses or other incentives***
Stefanini takes pride in hiring top talent and developing relationships with our future employees. Our talent acquisition teams will never make an offer of employment without having a phone conversation with you. Those face-to-face conversations will involve a description of the job for which you have applied. We also speak with you about the process, including interviews and job offers.
About Stefanini Group
The Stefanini Group is a global provider of offshore, onshore and near shore outsourcing, IT digital consulting, systems integration, application, and strategic staffing services to Fortune 1000 enterprises around the world. Our presence is in countries like the Americas, Europe, Africa, and Asia, and more than four hundred clients across a broad spectrum of markets, including financial services, manufacturing, telecommunications, chemical services, technology, public sector, and utilities. Stefanini is a CMM level 5, IT consulting company with a global presence. We are a CMM Level 5 company.
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