Location: Anywhere in LATAMWork Model: 100% RemoteEngagement: Full-time ContractorPayment: USDProject: Identity Security / Cybersecurity PlatformSeniority: SeniorLocation: Anywhere in LATAMWork Model: 100% RemoteEngagement: Full-time ContractorPayment: USDProject: Identity Security / Cybersecurity PlatformSeniority: SeniorEnglish Level: B2 / C****L - Senior DevOps Engineer (AWS/EKS/Terraform/CI/CD) · Remote · LATAMGet to Know DarwoftAt Darwoft, we build custom software solutions and partner with international companies to create high-impact digital products.We are a Latin American technology company with a people-first culture, strong professional standards, and a long-term partnership mindset.
We workment novel serving strategies and inference algorithms.
Your responsibilities include engineering robust inference pipelines, establishing comprehensive performance metrics, and identifying and resolving bottlenecks in production environments.
The ultimate goal is to enable high‐throughput, low‐latency, low‐memory footprint, and scalable AI performance that delivers tangible value in dynamic, real‐world scenarios.ResponsibilitiesDesign and deploy state‐of‐the‐art model serving architectures that deliver high throughput and low latency while optimizing memory usage.
Ensure these pipelines run efficiently across diverse environments, including resource‐constrained devices and edge platforms.
Establish clear performance targets such as reduced latency, improved token response, and minimized memory footprint.Build, run, and monitor controlled inference tests in both simulated and live production environments.
Track key performance indicators such as response latency, throughput, memory consumption, and error rates, with special attention to metrics specific to resource‐constrained devices.
Document iterative results and compare outcomes against established benchmarks to validate performance across platforms.Identify and prepare high-quality test datasets and simulation scenarios tailored to real‐world deployment challenges, specifically those encountered on low‐resource devices.
Set measurable criteria to ensure that these resources effectively evaluate model performance, latency, and memory utilization under various operational conditions.Analyze computational efficiency and diagnose bottlenecks in the serving pipeline by monitoring both processing and memory metrics.
Address issues such as suboptimal batch processing, network delays,



and high memory usage to optimize the serving infrastructure for scalability and reliability on resource‐constrained systems.Work closely with cross‐functional teams to integrate optimized serving and inference frameworks into production pipelines designed for edge and on‐device applications.
Define clear success metrics such as improved real‐world performance, low error rates, robust scalability, optimal memory usage and ensure continuous monitoring and iterative refinements for sustained improvements.A degree in Computer Science or related field.
Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D; (with good publications in A* conferences).
Must have knowledge of Metal Shading Language (MSL).
You should be comfortable writing custom compute shaders from scratch.Proven experience in low‐level kernel optimizations and inference optimization on mobile devices is essential.
Your contributions should have led to measurable improvements in inference latency, throughput, and memory footprint for domain‐specific applications, particularly on resource‐constrained devices and edge platforms.A deep understanding of modern model serving architectures and inference optimization techniques is required.
This includes state‐of‐the‐art methods for achieving low‐latency, high‐throughput performance, and efficient memory management in diverse, resource‐constrained deployment scenarios.Must have strong expertise in writing GPU kernels for mobile devices (i.e., smartphones) as well as a deep understanding of model serving frameworks and engines.
Practical experience in developing and deploying end‐to‐end inference pipelines, from optimizing models for efficient serving to integrating these solutions on resource‐constrained devices is required.Demonstrated ability to apply empirical research to overcome challenges in model serving, such as latency optimization, computational bottlenecks, and memory constraints.
You should be proficient in designing robust evaluation frameworks and iterating on optimization strategies to continuously push the boundaries of inference performance and system efficiency.Distributed Inference Systems:



Designing and optimizing high‐performance inference engines using techniques lionBashExperience implementing observability using technologies such as:PrometheusGrafanaCloudWatchOpenSearch / ElasticsearchELKStrong understanding of networking concepts including:VPCsSubnetsLoad BalancersDNSVPNsFirewallsRoutingSecurity GroupsExperience troubleshooting complex distributed systems and production environments.Solid understanding of cloud security and infrastructure security best practices.Experience supporting highly available, production-grade cloud environments.Strong communication skills and ability to collaborate with engineering, security, QA, and business stakeholders.High degree of autonomy, ownership, and analytical problem-solving ability.Bachelor's degree in Computer Science, Software Engineering, Systems Engineering, or a related technical discipline, or equivalent professional experience.Core Technology StackYou'll work with technologies including:AWSKubernetes / EKSDockerTerraformAnsibleGitHub ActionsJenkinsLinuxPythonBashPrometheusGrafanaCloudWatchOpenSearch / ElasticsearchNice to HaveExperience with HashiCorp Nomad .
Experience with HashiCorp Consul .
Broader experience with the HashiCorp ecosystem.Experience operating Kafka in production.Experience supporting data infrastructure based on:ClickHouseElasticsearchMongoDBRedisExperience with distributed systems and high-volume data platforms.Previous experience in cybersecurity, identity security, or security SaaS platforms .
Experience working with cloud security controls and compliance frameworks.Experience with FinOps and cloud cost optimization .
Experience designing internal developer platforms or platform engineering capabilities.Exposure to infrastructure supporting AI/ML or LLM-based applications .
What Success Looks LikeSuccess in this role means:Increasing infrastructure reliability and availability.Reducing deployment friction and improving engineering velocity.Increasing infrastructure automation and reducing manual operational work.Improving observability and reducing mean time to detection and resolution.Operating Kubernetes workloads reliably at scale.Strengthening security throughout cloud infrastructure and deployment processes.Improving AWS cost efficiency without compromising reliability.Contributing reusable platform capabilities that allow engineering teams to move faster.Continuously improving production readiness as the platform scales.What Darwoft OffersContractor agreement with payment in USD100% remote workArgentina's public holidaysEnglish classesReferral programAccess to learning platforms
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📌 Aws Devops/Sre Engineer (Devsecops) – Remote (La Pampa)
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