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Senior Engineer (Applied AI / ML)
Qvest.us
Remote, USA
Full-time
Senior · 4+ years experience
Remote
Discovered 1 weeks ago
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Key Skills for This Role
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- You will provide leadership within software teams; as a mentor, team-lead, trusted client-facing technologist and/or as a hands-on contributor
- You will impact all phases of our projects and all phases of the SDLC
- You will leverage your existing depth of knowledge to maximize your impact
- You will grow into new and/or adjacent technologies and domains to broaden your impact
- Present solutions to clients as well as Qvest team members
- Shape the kind of technologies that we are going to be using
- 4-6 years in software development, including working in teams
- Experience with agile methodologies such as Scrum, Kanban or XP
- Experience in ML / Applied AI tools and patterns including LLM, MLOps, RAG
- Experience using different models (OpenAI, ChatGPT, Claude, Amazon Bedrock, LLaMA etc..) and understanding applicability to purpose / use case balancing cost vs. functionality
- Experience developing copilots
- Experience developing RAG applications
- Experience with and around discussing enterprise deployments, knowing the landscape of enterprises, (their API’s, systems etc..) and the ability to communicate with client leadership in this area
- Experience with Vector Database and Embeddings
- Experience with Graph Databases
- Definition, utilization and implementation of CI/CD pipelines
- Familiarity with SOA, Microservices, or similar architectural patterns
- Experience constructing systems by utilizing a separation of concerns (good OOP, FP design, etc)
- Experience with enterprise application integration, including defining the integration architecture, integration data flows and interfaces, integration mechanisms (e.g., async vs. synchronous), middleware, etc.
- Experience with typical AuthN/AuthZ methods and products
- Experience managing data, including the selection of persistence product, design of database schema, constraints and transaction boundaries, read/write design trade-off decisions and how it relates to mutable vs. immutable data state
- Experience balancing feature development vs. technical debt accumulation in order to deliver business needs while also maintaining quality over time
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