Software Engineer (AI/ML)

Skills

Description

Collaborate with team and with partners in QSDG and Platform to define, build, test, and deploy platform meeting requirements
Define & enforce standards & best practices related to platform management
Evaluate third-party products to meet scalability, resiliency, and performance
Build new or leverage existing platforms (Lab, SDLC) by automating setup, installation, verification, monitoring & provisioning processes
Maintain a central, version controlled, inventory of all environments, including their current versions and configuration settings
Plan & allocate environments to teams depending on their delivery lifecycle
Analyze data to identify and proactively address environment-related issues
Work with project teams to manage costs & improve efficiency of environments
Partner closely with Prod Support and Engineering to deploy & support applications

Responsibilities

• 7-10 years in similar roles, preferably in the financial industry
• Higher education in IT field or relevant previous work experience
• Prior experience designing, implementing, and maintaining end to end environments, from POC to production
• Deep understanding of hardware, software, network, data & application configuration
• DevOps processes and CICD tooling (Jira, Git/Bitbucket, Jenkins, Datival, Artifactory, Ansible), orchestration & automation
• Multi-tier (Python based) web application stack microservices/serverless/loosely coupled architecture
• Mix of on-premises and cloud based, containerized (Docker/Kubernetes/OpenShift) deployment models
• Familiarity with no-SQL (MongoDB) and relational (SQL Server/Oracle) databases, and other various forms of Object, Vector, and file stores
• Unix scripting, SQL, work scheduling tools
• Setting up infrastructure monitoring & reporting for GPU/CPU & memory consumption, inference latency and model performance
• Performance profiling & optimization techniques to maximize performance & resource consumption / throughput and minimize latency
• Load balancing, high availability & backup recovery strategies/techniques
• Ability to communicate effectively to a wide range of audience (business stakeholders, developer & support teams)
• Meticulous & highly organized
• Adaptable to shifting & competing priorities
• Skilled at delegating, mentoring & setting expectations
• Critical thinking skills to diagnose & resolve complex issues

Educational Requirements

• Familiarity with AI & Deep learning, modeling techniques, Generative AI application stack
• Proficiency in Python and familiarity with AI frameworks (TensorFlow/PyTorch)
• GPU cluster management (CUDA/Kubernetes), auto-scaling & scheduling (Triton Inference Server)