Senior Engineer (Applied AI / ML)

Qvest US is a global leader in technology and business consulting for the Media & Entertainment and Consumer Packaged Goods & Retail industries. As a Senior Engineer in the Applied AI / ML practice, you will provide leadership within software teams, mentor colleagues, and influence technologies and solutions for both internal and client projects.

Responsibilities:

• 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

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