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# Datadog Engineer

- **Company:** OpenKyber LLC
- **Location:** Atlanta, GA, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Airflow, Amazon Web Services, Cloud Computing, Cloud Engineering, Computer Programming, Continuous Integration, Data Stores, Cursor, DevOps, Disaster Recovery, Distributed Systems, Python (Programming Language), Machine Learning, NoSQL, Site Reliability Engineering Practices, Prometheus, Reinforcement Learning, Datadog, Aerospike, Google Cloud, Feature Engineering, Grafana, Apache Spark, Gitlab, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Low Latency, Apache Flink, Multi-objective Optimization, Apache Kafka, Machine Learning Operations, Claude, Drift Detection, Terraform, GPT, Jenkins
- **Published:** October 11, 2026
- **Apply:** https://www.careerjet.com/jobad/us1cfa0e2ef4ae1e5f63f52bdeb9386996

## About the Role

We're excited if you have BS or MS in Computer Science, Engineering, or a related quantitative field
Ability to demonstrate AI tool (Claude, Cursor, ChatGPT etc.) coding efficiency at an advanced skill level 8+ years of experience in DevOps, SRE, or ML infrastructure, including 4+ years supporting large-scale ML or AI systems
Strong programming skills in Python and/or Scala or Java for platform automation and tooling
Deep experience with Kubernetes and container orchestration on Google Cloud Platform (GKE) and/or AWS (EKS)
Expertise with NoSQL or low-latency data stores such as Aerospike or similar technologies
Hands-on experience with data and orchestration technologies such as Apache Spark, Apache Flink, Apache Airflow, and Kafka
Experience building and maintaining CI/CD systems using tools such as Jenkins or GitLab Runner
Familiarity with feature engineering platforms such as Chronon and model lifecycle tools such as MLflow
Strong infrastructure-as-code experience with Terraform or similar tooling
Experience with observability platforms such as Prometheus, Grafana, and Datadog
Excellent communication and cross-functional collaboration skills
Experience in the Advertising domain is a plus #LI-DH2

## Description

The Advertising Performance group focuses on performance for all participants in the Advertising ecosystem - Advertisers, Publishers, and OpenKyber. The systems and solutions span multiple disciplines and technologies to perform real-time multi-objective optimization across distributed systems at large scale and with low latency. We use Machine Learning, Reinforcement Learning, AI, Control and Optimization Systems, and Auction Dynamics to solve a large set of complex problems. At the core of this is our Machine Learning, Experimentation, and Inference Platform, which powers the entire landscape, and we continuously evolve. About the role:
We are seeking a talented and experienced Senior Software Engineer, MLOps/DevOps, to join the Advertising Performance team and play a critical role in supporting and scaling our Machine Learning infrastructure. The ideal candidate has a strong background in DevOps/SRE practices, cloud infrastructure management, and MLOps tooling - with a passion for building platforms that accelerate ML experimentation and deployment at internet scale. You will partner closely with ML Scientists and Engineers to streamline the end-to-end ML lifecycle across training, evaluation, deployment, and monitoring - on top of a modern, cloud-native stack running on Google Cloud Platform and AWS using Kubernetes, Apache Airflow, Spark, Ray, MLflow, Chronon, etc.

 * Lead the design and operation of scalable, production-grade cloud infrastructure for ML workloads across AWS and Google Cloud Platform, including GPU/TPU-based training and inference environments
 * Architect and improve CI/CD systems for ML models and platform services to enable fast, reliable, and safe production releases
 * Own and evolve low-latency infrastructure for real-time model inference, including KV store and vector databases
 * Define and enforce observability standards for ML systems, including model performance monitoring, drift detection, capacity planning, and pipeline health metrics
 * Participate in on-call rotation, leading incident response and root-cause analysis for critical ML training and serving infrastructure
 * Partner with data scientists and ML engineers to improve platform usability, accelerate model iteration, and implement strong MLOps and SRE best practices
 * Champion operational excellence across ML infrastructure through automation, resilience engineering, disaster recovery planning, and continuous improvement