Senior Cloud Dev/Ops Engineer

Soros Fund Management•New York, NY

5h•Onsite

About The Position

We are seeking a mid-to-senior level engineer to join our Cloud SRE & DevOps Engineering team in our New York City office (primary site), focused on building, operating, and evolving the AI, cloud infrastructure and delivery platforms that support our trading and investment systems. This role sits at the intersection of AI Infrastructure Engineering, Cloud Engineering, SRE, DevOps, Platform Engineering, and Production Engineering, and is designed for individuals who take ownership of systems running in production. You will be responsible for designing and operating resilient, scalable environments across AWS and Kubernetes, while enabling engineering teams through modern DevOps and GitOps practices. The position reflects a strong Site Reliability Engineering (SRE) mentality, with emphasis on reliability, observability, automation, and operational excellence. You will play a key role in advancing the firm’s cloud transformation strategy, ensuring systems are built for performance, stability, and scale. This is a hands-on role requiring deep technical expertise, accountability for production systems, and a mindset oriented toward continuous improvement, risk management, and engineering efficiency. The role also contributes to evolving platform capabilities supporting AI and data-driven workloads. You will work closely with software engineering, cybersecurity, and data teams to deliver secure, scalable, and high-performing systems, while improving developer experience and platform maturity across the organization.

Requirements

Strong hands-on experience with AWS in production environments
Experience working in hybrid or multi-cloud environments (AWS and Azure)
Deep experience with Kubernetes and containerized systems (Docker, EKS)
Strong familiarity with Kubernetes tooling, including Helm
Proven experience with infrastructure as code (Terraform preferred)
Strong experience building and managing CI/CD pipelines (GitHub Actions or similar)
Proficiency in Python, Shell, or PowerShell for automation
Strong understanding of distributed systems, networking, and cloud architecture
Experience operating and supporting production systems with high availability and performance requirements
Experience diagnosing and resolving complex production incidents in distributed environments
Experience supporting relational databases (e.g., PostgreSQL, MySQL, SQL Server, Aurora) in cloud environments
Hands-on experience with observability tools such as Datadog, AWS CloudWatch, Geneos, or LogicMonitor (or comparable enterprise monitoring platforms), including designing metrics, alerts, and dashboards for production systems
Strong grounding in DevOps methodologies, including automation, continuous delivery, and infrastructure standardization
Experience working with Git-based workflows and modern software engineering practices
Ability to balance speed, stability, and risk in production environments
Strong analytical and troubleshooting skills
Ability to operate effectively in high-performance, time-sensitive environments
Clear communication across technical and non-technical stakeholders
Ownership mindset with accountability for critical systems
Bachelor's degree in Engineering, Computer Science, Information Systems, or equivalent experience.
8+ years of experience required
Hands-on experience with cloud platforms such as AWS, Azure, and Snowflake, with a solid understanding of IaaS, PaaS, and SaaS in hybrid environments.
Experience in cloud infrastructure or server-side development, with a fundamental understanding of containerization and Kubernetes.
Proficiency in programming languages like PowerShell and Python, with experience in DevOps tools such as Terraform, Docker, and Kubernetes.
Deep understanding of CI/CD tools (e.g., Octopus, Bamboo, Azure DevOps, Git Actions) and configuration management.
Expertise in automation scripting, infrastructure as code (IaC), and immutable infrastructure using tools like AWS CloudFormation or Terraform.
Familiarity with observability frameworks (DataDog, OpenSearch, and LogicMonitor).
Familiarity with networking, storage, and database concepts.
Ability to craft clear, effective communications for both technical and non-technical audiences.
Strong analytical and troubleshooting skills, attention to detail, ability to multitask, and work effectively in a fast-paced environment.
Proficiency in writing scripts using Python, PowerShell, Bash, and experience with technologies like Docker, Lambda functions, and Kubernetes.

Nice To Haves

Experience supporting AI/ML workloads or data platforms
Familiarity with LLM APIs, vector databases, or GPU-based environments
Exposure to modern data platforms such as Databricks, Google BigQuery, or Amazon Redshift
Experience supporting trading or investment systems
Understanding of trading workflows (OMS, order entry, trade booking), market data, or FIX protocol
Hands-on experience with Terraform, Packer, Git, and Jira is considered a plus.
Integration with JIRA and GitHub is a plus
Experience in designing, developing, and implementing ETL/ELT processes using Snowflake and related technologies.

Responsibilities

Design, build, and operate scalable AWS-based infrastructure supporting trading systems
Work across hybrid or multi-cloud environments (AWS and Azure)
Manage and optimize Kubernetes environments (EKS), ensuring resilience, scalability, and performance
Utilize Kubernetes ecosystem tooling (e.g., Helm) to support application deployment and lifecycle management
Develop reusable infrastructure using Terraform and infrastructure as code principles
Manage the automated delivery of Snowflake configurations through CICD pipelines
Help administration of Microsft SQLServer, DataBricks, Snowflake and AWS (Aurora/RDS)
Design and maintain CI/CD pipelines using tools such as GitHub Actions
Promote Git-based workflows and support GitOps practices (e.g., ArgoCD)
Improve deployment reliability, consistency, and engineering velocity
Own and operate business-critical production systems with a strong focus on uptime, performance, and risk mitigation
Troubleshoot and resolve complex issues across distributed systems, cloud infrastructure, and Kubernetes environments
Implement and enhance monitoring, logging, and alerting using tools such as Datadog, AWS CloudWatch, Geneos, and LogicMonitor
Apply cloud security best practices, including IAM, secrets management, and vulnerability scanning
Support and optimize relational database systems (e.g., PostgreSQL, MySQL, SQL Server, Aurora), ensuring performance and high availability
Contribute to backup, recovery, and resilience strategies across infrastructure and data layers
Drive improvements aligned with SRE principles, including reliability, observability, and operational maturity
Improve developer experience through automation, standardization, and self-service capabilities
Partner with engineering teams to streamline development and deployment workflows
Support infrastructure for AI/ML and data-driven workloads, including scalable compute and data processing patterns
Experience with AWS Bedrock or Sagemaker, with AgentCore/KnowledgeBases/RAG setup
Enable deployment patterns for modern, data-intensive applications
Evaluate emerging technologies relevant to AI-enabled platforms