Lead Machine Learning Engineer (CCAI, Dialog Flow)
Egen is a fast-growing and entrepreneurial company with a data-first mindset. We bring together the best engineering talent working with the most advanced technology platforms, including Google Cloud and Salesforce, to help clients drive action and impact through data and insights.
We are committed to being a place where the best people choose to work so they can apply their engineering and technology expertise to envision what is next for how data and platforms can change the world for the better.
We are dedicated to learning, thrive on solving tough problems, and continually innovate to achieve fast, effective results.
We are looking for a Machine Learning Engineer to assist in developing implementation patterns across natural language and generative AI practices.
You will be responsible for delivering systems design and architecture, documenting requirements, and making design decisions.
Responsibilities :
- Lead delivery teams and clearly delineate parallel work-streams
- Lead discovery sessions with clients and carefully navigate requirement discussions to avoid designs requiring additional scope
- Support pre-sales discussions and assist pre-sales engineers to identify scope issues and clearly define assumptions for LOEs
- Drive reusable code and solution architecture practice
- Apply cloud architecture and application design knowledge
- Assist the sales team with potential clients
- Document requirements and design decisions
- Ensure coding practices result in clean, reusable code to consistently mature the delivery practice
What we're looking for :
- 5+ years of experience as a ML Engineer
- Bachelor's Degree is preferred but will consider relevant experience as an equivalent
- Strong foundation in NLP, vision, and generative AI
- Vertex AI, Vertex Search and Conversations
- Dialogflow CX
- Vision AI and OCR
- Cloud Text-to-Speech, Cloud Speech-to-Text
- Cloud Translation, Glossaries
- Python
- Vector stores
- Langchain
- Data Loss Prevention
- CICD best practices, especially with complexity of CCAI and / or PSO engagements
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