We are seeking an Applied Scientist - AI/ML Engineer to maximize the potential of large language models through advanced prompt engineering and a deep understanding of video workflows.
In this role, you will heavily iterate on Amazon Bedrock models to optimize outcomes by refining prompts that detect sports moments using transcripts and video frames within a multi-modal framework. You will process test video assets through Amazon Bedrock for custom moments detection, configure and optimize models to maximize accuracy, and build repeatable templates for pipeline processing and structured metadata output.
Responsibilities
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Design and optimize prompts for multimodal foundation models (Claude, Nova, etc.) to detect custom ad moments in live video frames and audio transcripts
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Conduct model comparison studies with accuracy benchmarking on domain-specific content
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Configure and tune the Bedrock Connector pipeline for each custom moment type
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Validate detection accuracy across multiple sports, genres, and live event types
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Fine-tune prompts iteratively to improve detection accuracy and outcomes
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Collaborate with the AWS Elemental Inference team to align on technical direction
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Develop Python scripts to automate custom moments pipelines when needed
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Build repeatable templates for pipeline processing and structured metadata output
Requirements
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3+ years of experience in applied AI/ML with a background in multimodal models (vision + language)
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Hands-on expertise in prompt engineering for large foundation models, preferably using Amazon Bedrock
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Proficiency in Python for scripting and pipeline automation
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Familiarity with video/image understanding in live video workflows and near-real-time inference pipelines
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Ability to design evaluation frameworks and benchmark model accuracy
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Experience working in media, advertising, or content classification
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Understanding of Media Supply Chain concepts and workflows
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Proficiency in English at an Upper-Intermediate level (B2) or higher
Nice to have
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Familiarity with working with video frames using OpenCV and transcript data
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Knowledge of computer vision and natural language processing techniques
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Prior experience in sports analytics or media technology
We offer
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International projects with top brands
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Work with global teams of highly skilled, diverse peers
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Healthcare benefits
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Employee financial programs
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Paid time off and sick leave
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Upskilling, reskilling and certification courses
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Unlimited access to the LinkedIn Learning library and 22,000+ courses
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Global career opportunities
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Volunteer and community involvement opportunities
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EPAM Employee Groups
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Award-winning culture recognized by Glassdoor, Newsweek and LinkedIn
EPAM is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity or expression, disability, protected veteran status, or any other characteristic protected by applicable law.
