A technology company in the iGaming sector is looking for a Principal DevOps Engineer to lead the design and development of a new cloud infrastructure platform. This is a greenfield role focused on AWS, Kubernetes, infrastructure automation, CI/CD, observability, reliability, and highly available production environments. The position requires a hands-on technical leader who can define platform standards while continuing to work directly with infrastructure code. Main Responsibilities: Design a scalable AWS environment, including multi-account architecture, networking, IAM, governance, and cost controls. Build and maintain a Kubernetes platform based on Amazon EKS. Implement infrastructure as code using Terraform and GitOps delivery practices. Create reliable CI/CD pipelines and developer-friendly deployment standards. Establish monitoring, logging, tracing, alerting, SLOs, and operational reliability practices. Define incident management, disaster recovery, backup, runbook, and production-readiness processes. Support migration from an external platform to newly built internal infrastructure. Ensure the platform meets security, audit, and regulatory requirements. Mentor engineers and establish cloud and DevOps standards across the engineering organization. Desired experience: Strong hands-on AWS knowledge, particularly EKS, EC2, RDS/Aurora, VPC, Transit Gateway, Route 53, IAM, and multi-account environments. Expert-level Terraform and strong Kubernetes production experience. Proven experience designing CI/CD and internal developer platforms. Experience supporting highly available, high-throughput systems with significant traffic peaks. Strong Python, Go, or Bash scripting skills. Experience with backup, disaster recovery, and business continuity.      

Quantitative Trader (Prediction Markets) — Remote Europe A fully remote trading company operating in prediction markets is looking for a Quantitative Trader to join a senior team building high-performance automated trading systems. The business is backed by an established tier 1 iGaming and sports betting operator and benefits from significant industry expertise, technology, and resources. The role is focused on overseeing highly automated live trading, improving trading strategies, identifying and validating edge, and safely rolling improvements into production. Performance will be measured through areas such as EV gains, fill quality, and trading-system uptime. This is a live-markets position. It is not a traditional 9-to-5 role, and some weekend coverage is expected as part of the trading team's shared schedule. Main Responsibilities: Monitor live automated trading, including performance, exposure, market behaviour, and risk during sporting events. Adjust algorithmic trading behaviour when required and intervene through hedging or halting when markets, models, or data behave unexpectedly. Research, test, and improve pricing, signals, quoting strategies, models, and risk logic across sports prediction markets. Manage the safe rollout of strategy changes using shadow, canary, or staged-exposure approaches. Analyse trading data including P&L attribution, fill quality, slippage, exposure, and model performance. Backtest and simulate strategy changes before they are introduced into live markets. Work closely with trading-systems engineers to translate strategy ideas into production logic and improve trading tools, controls, and guardrails. Contribute trader judgement to product decisions around instrument design, pricing, settlement, and risk. Desired experience: Strong sports betting domain knowledge is essential. Experience within a sportsbook, betting exchange, betting operator, financial trading environment, or a demonstrable personal betting track record. Good understanding of probability, statistics, expected value, edge, risk, and market dynamics. Comfortable analysing trading data using Python and/or SQL. Experience using AI tools to support research, analysis, and reporting. Python skills are sufficient for the role. C#/.NET knowledge is advantageous but not mandatory. Quantitative background through Mathematics, Computer Science, Engineering, Statistics, Economics, or equivalent practical experience. Willingness and genuine interest in working around US sporting events, including some weekends. Comfortable working with ownership and ambiguity in an early-stage environment. Experience with prediction markets, exchange-style products, sports modelling, machine learning, automated trading strategies, BI/reporting, or C#/.NET would be advantageous but is not 100% mandatory.