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Senior Engineer – Applied AI, ML

Full-stack EngineerSoftware EngineerFull TimeRemoteTeam 201-500Since 2013H1B SponsorCompany Site LinkedIn

Location

United States

Posted

129 days ago

Salary

Not specified

Bachelor Degree4 yrs expEnglishMicroservicesSDLC

Job Description

• Provide leadership within software teams; as a mentor, team-lead, trusted client-facing technologist and/or as a hands-on contributor • Impact all phases of our projects and all phases of the SDLC • Leverage your existing depth of knowledge to maximize your impact • 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

Job Requirements

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.