Senior AI Engineer – Sfl Scientific

Senior AI Engineer at Deloitte's SFL Scientific, focusing on developing robust AI infrastructure and deployment services for machine learning applications. The role involves designing and leading the development of scalable data architecture solutions for AI/GenAI use cases, leveraging cloud and on-prem technologies. Responsibilities include data engineering, architecture design, deployment, and mentoring junior engineers, with applications in areas like cancer detection, drug discovery, and autonomous systems.

What you'd actually do

  1. Work with clients to design, develop, and deploy new architectures to support machine learning & automation applications
  2. Leverage advanced technical skills in modern data architecture, data science engineering, data transformation, and management of structured and unstructured data sources using cloud computing or on-prem technologies
  3. Design and lead development on scalable, high-performance data architecture solutions that supports both the client business as well as AI/GenAI use cases
  4. Support and enhance data architecture, and data pipelines, and define database schemas (Graph, SQL, NoSQL) to develop algorithm scalability and deployment based on agile business priorities and initiatives
  5. Participate in architectural and deployment discussions to ensure solutions are designed for successful scale, security, and high availability in the cloud or on prem

Skills

Required

  • modern data architecture
  • data science engineering
  • data transformation
  • management of structured and unstructured data sources
  • cloud computing
  • on-prem technologies
  • scalable, high-performance data architecture solutions
  • data architecture
  • data pipelines
  • database schemas (Graph, SQL, NoSQL)
  • algorithm scalability and deployment
  • architectural and deployment discussions
  • automation
  • HPC
  • AI/GenAI infrastructure
  • design patterns
  • technology proof of concepts
  • data and cloud technology solutions
  • modern data architecture principles
  • technology modernization

Nice to have

  • Ability to work independently and collaborate as part of a team
  • Effective written and verbal communication skills
  • Meticulous attention to detail and quality of work product
  • Ability to build and sustain professional relationships
  • Ability to lead projects or workstreams
  • Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
  • Strong interpersonal skills and professional demeanor
  • Ability to meet deadlines
  • Ability to provide clear guidance to others

What the JD emphasized

  • AI/GenAI use cases
  • AI/GenAI infrastructure

Other signals

  • deploying AI/ML applications
  • scalable, high-performance data architecture solutions
  • AI/GenAI use cases
  • AI/GenAI infrastructure