AI/ML Data Engineering Manager – SFL Scientific
SFL Scientific, a Deloitte Business practice brings together several key capabilities to architect integrated programs that transform our clients' businesses, including Strategic Growth Transformation, Transformation Strategy & Design, Technology Strategy & Business Transformation, and AI & Data Strategy.
Professionals will serve as trusted advisors to our clients, working with them to make clear data-driven choices about where to play and how to win – ultimately driving growth and enterprise value.
We are hiring a Data Engineering Manager to support the design, development, and deployment of novel AI solutions across healthcare, life sciences, manufacturing, energy, and other sectors.
Work you’ll do
As a Data Engineering Manager, you will lead client engagements around the design and delivery of innovative solutions for complex R&D type problems. You will be responsible for the technical direction of projects while engaging with internal stakeholders, understanding business priorities, defining the data strategy, communicating complex technical concepts, and leading application deployment in order to solve our clients’ use cases. You will work cross-functionally with data scientists, project managers, and industry experts to develop robust data platforms and cloud solutions.
In our consultative approach, we are platform agnostic and are committed to accelerating the development of innovative AI solutions for our clients with the best possible tools; this spans all relevant technologies from on-prem and cloud deployment, high performance computing, automation, DevOps, MLOps, data engineering and streamlining IT infrastructure processes. Join us to expand your technical career through leadership, consulting, and becoming an industry leader in the AI engineering community.
- Work with clients to design, develop, and deploy new architectures for machine learning & automation applications such as ELT functions, HPC/compute infrastructure, hybrid cloud solutions, database management, and optimization of DevOps procedures
- 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
- Support and enhance data architecture, and data pipelines, and define database schemas (Graph DB, SQL, NoSQL) to develop algorithm scalability and deployment based on agile business priorities and initiatives
- Participate in architectural discussions to ensure solutions are designed for successful deployment, security, and high availability in the cloud or on-prem
- Adopt best engineering practices in automation, HPC and AI Infrastructure best practices
- Present to key stakeholders, including architecture findings and design of infrastructure, hardware, software, cloud, and deployment, etc.
- Mentor, motivate and coach junior members on technical best practices and inspire professional development
The Team
SFL Scientific, a Deloitte Business, is a data science professional services practice focused on strategy, technology, and solving business challenges with Artificial Intelligence (AI). The team has a proven track record serving large, market-leading organizations in the private and public sectors, successfully delivering high-quality, novel and complex projects, and offering deep domain and scientific capabilities. Made up of experienced AI strategists, data scientists, and AI engineers, they serve as trusted advisors to executives, helping them understand and evaluate new and essential areas for AI investment and identify unique opportunities to transform their businesses.
Basic Qualifications:
- Bachelor's degree in a STEM field or equivalent experience (Data Science, Computer Science, Engineering, Physics, Mathematics, etc.); Master’s degree is preferred
- 6+ years’ experience in data engineering, software engineering, MLOps, or data science
- Strong management skills with experience managing teams and delivering complex and critical projects
- Expert programming skills in Linux Shell/CLI, Python, Powershell, etc
- Experience with enabling modern deep learning software architectures and frameworks including Tensorflow, PyTorch or other frameworks
- Experience managing Docker, Kubernetes, Spark, Dask, Flask, and CI/CD services
- Experience with provisioning and configuration management tools; Puppet, Ansible, Chef, Airflow, Terraform, Jenkins etc.
- Experience with Databases (relational and NoSQL) and data warehousing
- Experience with deployment and optimization–Kubernetes, Docker, NVIDIA TensorRT/Triton, RAPIDs, Kubeflow, MLflow etc.
- Proficient of the various services and capabilities of computing platforms (AWS/Azure/GCP and Nvidia suite), specifically EC2, EBS, ELB, RDS, S3, Redshift, Lambda, etc Experience with workflow and data management solutions such as Airflow, Kafka, Glue, etc.
- Limited immigration sponsorship may be available
- Ability to travel 10%, on average, based on the work you do and the clients and industries/sectors you serve
- Live within commuting distance to one of Deloitte’s consulting offices
Preferred Qualifications:
- Excellent verbal and written communication skills and experience in a client-facing or team management role is preferred
- Expert with GPU computing (CUDA, OpenCL) and HPC system software stack
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $168,000 to $280,000.
You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.
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