Senior Machine Learning Engineer
Role details
Job location
Tech stack
Job description
Design, develop, and implement machine learning pipelines and GenAI-powered search and conversational chatbot solutions for scalable mobile and TV applications at Sky and Comcast .
As a professional, be a source of expertise and knowledge for junior team members. Solve complex problems within your area, coordinating with others outside of it if needed. Lead on projects or parts of projects.
What you'll do
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Design, develop and optimise GenAI-powered search and conversational chatbot experiences that integrate seamlessly into scalable mobile and TV applications used by end users
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Build retrieval-augmented generation (RAG) pipelines combining LLMs with structured and unstructured data
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Optimise relevance, latency, and response quality for interactive UI components
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Evaluate and improve models using real-world feedback, analytics and experimentation
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Collaborates closely with frontend, product, and UX teams to embed AI components into user-facing applications
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Contributes to system architecture, model selection, and deployment strategies
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Provides technical guidance and mentorship to junior members of the engineering team
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Participates in Scrum / agile process
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Participates in on-call support with the rest of the team
Requirements
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Extensive e xperience with traditional ML system s and GenAI ( LLMs, agent frameworks ), including proven delivery of GenAI powered search or chatbot solutions in production
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Working experience and in-depth understanding of:
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Large Language Models (LLMs)
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Vector databases and semantic search
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Retrieval-Augmented Generation (RAG)
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Extensive programming expertise , primarily in Python, delivering ML and GenAI solutions with a language-agnostic mindset
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Familiar with cloud services (e.g. AWS Lambda best practices)
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Strong team player and confident communicator with experience as a tech lead and individual contributor in agile, fast paced environments
Big Plus:
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PhD in related subjects
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Extensive machine learning research background, including a cademic publications in ML- related conferences or journals