Machine Learning Architect (AWS)

Rackspace

United States

United States

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We are expanding our team of motivated technologists with a proven track record of delivering results in technology consulting. We are looking for a Machine Learning Architect with experience in cloud (AWS preferred) who is passionate about helping customers build AI/ML solutions at scale. Being an experienced technologist with technical depth and breadth, aided with strong interpersonal skills, you will work directly with customers as part of a delivery team, helping to enable innovation by creating state of the art Machine Learning solutions that align to business goals. This role includes responsibilities both as a Professional Services Machine Learning Architect and as a hands-on Machine Learning engineer on customer engagements. The qualified Machine Learning Architect will have demonstrated the ability to think strategically about businesses, create technical definitions around customer objectives in complex situations, develop solution strategies, motivate & mobilize resources, and deliver results. The ability to connect technology with measurable business value is a critical component to be successful in this role. We seek team members who are self-motivated, driven, collaborative, passionate about machine learning, and want to have a direct positive impact on our customer's business. Strong communication skills and emotional intelligence are also needed to help develop a team that works with you. Work Location: Remote Key Responsibilities:
Design machine learning solutions and execute machine learning projects end to end from proof-of-concept stage to deployment in production using cloud native technologies and state of the art machine learning models. Be technically focused but work directly with the business representatives/customers to understand the requirements driving the need for a solution to be developed. Be responsible for all phases of the project from problem definition, data annotation, model development, model deployment to end user documentation/training. Design the architecture of ML solutions on cloud platforms (AWS, Azure, GCP) including MLOPs. Stay abreast of the latest developments. Read the latest published machine learning research and adapt the models to solve customer’s problems. Establish credibility by demonstrating technical excellence and delivering value through solutions you build. Develop strong relationships with our customers. Qualifications:
Masters with 10+ years of experience or PhD with 6+ years of experience in Machine Learning, Natural Language Processing (NLP) and Deep Learning. Minimum 5+ years of experience architecting and building Machine Learning solutions. Minimum 5+ years of experience with cloud platforms (AWS, GCP, Azure). Experience building ML models and strong knowledge of ML techniques is required. Experience with hugging face, TensorFlow/pytorch, transformer architectures, prompt engineering, agentic systems, LLMs. Strong coding experience in Python and architectural patterns like microservices. Solid understanding of agile methodologies and experience in planning machine learning projects from inception to production deployment. Strong problem-solving skills and the ability to lead a team on “what’s next” when encountering a technical issue in a machine learning project. Excellent communication and presentation skills, with the ability to explain complex technical concepts to both technical and non-technical audiences. Travel:
As per business requirements $153,000 - $244,700 a year
The following information is required by pay transparency legislation in the following states: CA, CO, HI, NY and WA. This information applies only to individuals working in these states.
The anticipated starting pay range for Colorado is: $153,000 - $204,000
The anticipated starting pay range for Hawaii and New York (not including NYC) is: $167,400 - $223,200
The anticipated starting pay range for California, New York City and Washington is: $183,500 - $244,700
Based on eligibility, compensation for the role may include variable compensation in the form of bonus, commissions, or other discretionary payments.
These discretionary payments are based on company and/or individual performance, and may change at any time.
Actual compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location.
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United States

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English

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