Machine Learning Engineer

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Responsibilities:

• Partner with technical (ML/AI) and product stakeholders to design an effective multimodal AI platform (audio, video and text generation)
• Design and implement necessary pipeline components to enable new use cases.
• Develop components for a multimodal Python pipeline that involves audio, video and text generation in a low latency, streaming regime
• Collect and analyze datasets stored in various databases (SQL, PostgreSQL and vector datastores such as Pinecone)

Requirements:

• Minimum of 5 years of relevant work experience
• Expertise in Data Pipeline Engineering: Skilled in developing and maintaining data pipelines to collect, process, and transform data; perform data manipulation and data analysis
• Expertise with Model Development Optimization: Adept at creating ML model prototypes (e.g. for classification), LLM workflows (e.g. using LLM agents) and/or performing model evaluation.
• Experience with Model Deployment Monitoring: Experience in implementing and deploying machine learning models into production, setting up robust monitoring systems to track performance and model drift.
• Experience with cloud computing (GCP or AWS) and data engineering
• Formal training in Computer Science or an Engineering discipline (Bachelor’s or Master’s)

You Should Be:

• Eager to move fast and thrive in a dynamic startup environment and achieve excellence with minimal supervision.
• Be able to deliver high-quality results with a keen eye for detail
• Collaborative and willing to support others and enhance the collective team performance
• Have strong verbal and written communication skills and able to articulate technical concepts to non-technical stakeholders
• Excited to automate things, including testing and verification of your work.

Originally posted on Himalayas