• Act as a proactive technical reference, influencing architectural decisions and fostering a DevOps culture across multiple development teams. • Architect, manage, and operate AWS/GCP services, ensuring seamless interoperability between platforms with Cross Connects and VPNs. • Design and provision scalable infrastructure for development teams using Terraform / Terragrunt and GitLab pipelines. • Drive investigations to optimise cloud costs through traffic analysis and CloudTrail optimization. • Drive AWS Organization account onboarding and enforce security best practices.

Role Description We are seeking an Infrastructure Automation Engineer who blends strong Python development skills with deep networking domain expertise to automate network configuration, change management, and observability across multi-vendor enterprise networks. The role focuses on building production-grade automation using modern network APIs, infrastructure-as-code, and CI/CD practices that bring software engineering discipline to network operations. The ideal candidate writes idiomatic Python, has shipped network automation in production, and combines networking depth with practical software-engineering rigor. In this role, you will work closely with cross-functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well-engineered solutions, and will be expected to raise the bar through code review, design review, and mentorship of more junior engineers. The successful candidate brings strong engineering discipline, a clear communication style, and a track record of shipping meaningful work that holds up well in production. Key Responsibilities - Design and develop network automation solutions in Python using Netmiko, Nornir, NAPALM, and Scrapli. - Build automation pipelines for configuration management, deployment, and validation across multi-vendor environments. - Develop integrations with network APIs including NETCONF, RESTCONF, gNMI, and vendor-specific APIs. - Implement source-of-truth platforms such as NetBox or Nautobot and drive automation from authoritative data. - Build infrastructure-as-code for network configuration using Ansible, Terraform, and Jinja2 templating. - Implement network testing and validation using pyATS, Genie, Batfish, and Suzieq. - Develop CI/CD pipelines for network changes including pre-deployment validation and rollback. - Build observability and telemetry pipelines using gNMI, streaming telemetry, and modern analytics platforms. - Implement intent-based networking patterns and self-healing network workflows that let the network express desired state declaratively and converge toward it automatically when drift or failures occur. - Automate compliance and audit workflows across the network estate, generating evidence on demand and continuously verifying that network configuration matches the policies the organization has committed to. - Partner with network engineering teams to identify and automate high-impact manual workflows. - Develop reusable libraries, frameworks, and abstractions adopted across multiple automation efforts. - Mentor network engineers on Python development, software engineering practices, and modern automation patterns. - Stay current with network automation tooling, vendor API evolution, and broader industry developments. Qualifications - Bachelor’s degree in Computer Science, Networking, or a related field. - Five or more years of network automation experience. - Strong Python development skills including OOP, testing, and packaging. - Deep networking fundamentals including routing, switching, and BGP. - Hands-on experience with Netmiko, NAPALM, Nornir, or Scrapli. - Experience with modern network APIs (NETCONF, RESTCONF, gNMI). - Strong experience with Ansible and Jinja2 templating for network automation. - Familiarity with NetBox, Nautobot, or other source-of-truth platforms. - Strong Git-based workflows and CI/CD experience. - Excellent communication and collaboration skills. Preferred Qualifications - Cisco DevNet or vendor automation certifications. - Experience with multi-vendor networks (Cisco, Arista, Juniper). - Familiarity with streaming telemetry and modern observability stacks. - Open-source contributions to network automation projects. - Experience with cloud networking automation in addition to on-premises. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] or contact us at (908) 505-3544. Learn more about Bright Vision Technologies at www.bvteck.com .

Role Description We are seeking an AI Infrastructure Engineer to build and operate the large-scale data systems that power modern AI training and evaluation pipelines. The role combines deep data engineering expertise with a strong understanding of AI workloads, focusing on ingestion, transformation, quality assurance, lineage, and high-throughput delivery of data to training jobs across diverse modalities. The ideal candidate has experience operating petabyte-scale data systems, strong software engineering fundamentals, and clear understanding of how data infrastructure choices propagate into model quality and training efficiency. Key Responsibilities - Design and operate large-scale data pipelines supporting AI training, evaluation, and continual improvement workflows. - Build ingestion systems for diverse modalities including text, image, audio, video, and structured signals. - Implement data cleaning, deduplication, filtering, and quality assurance at petabyte scale. - Develop dataset versioning, lineage, and provenance tracking systems suitable for reproducible training. - Build high-throughput data loading systems that maximize GPU utilization during training. - Implement labeling workflows, active learning pipelines, and human-in-the-loop data improvement systems. - Design storage architectures balancing cost, throughput, and latency across data tiers. - Build evaluation dataset construction pipelines with strict integrity and contamination controls. - Implement data privacy, redaction, and consent enforcement throughout the pipeline. - Collaborate with ML researchers and engineers to align data systems with model development needs. - Drive observability of data quality, drift, and pipeline health across the AI data estate. - Optimize cost and performance through compression, format selection, and caching strategies. - Document data systems, schemas, and operational procedures for broad internal use. - Stay current with AI data infrastructure research and emerging open-source tools. Qualifications - Bachelor’s or Master’s degree in Computer Science or a related field. - Six or more years of data engineering experience, with significant work supporting ML or AI workloads. - Strong proficiency in Python and at least one JVM or systems language. - Deep experience with modern data processing frameworks such as Spark, Ray, or Beam. - Hands-on experience operating petabyte-scale storage and pipeline systems. - Strong understanding of distributed systems, data modeling, and storage formats. - Experience with dataset versioning, lineage, and reproducibility for ML workflows. - Familiarity with high-throughput data loading for accelerator-based training. - Strong software engineering practices including testing, CI/CD, and code review. - Excellent communication and cross-functional collaboration skills. Preferred Qualifications - Experience with multimodal datasets at large scale. - Familiarity with data quality tooling and dataset evaluation methodology. - Exposure to privacy-preserving data systems and regulated data handling. - Open-source contributions to data infrastructure projects. - Experience supporting frontier model training pipelines. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] or contact us at (908) 676-4399. Learn more about Bright Vision Technologies at www.bvteck.com .

• Define and own the architecture vision, standards, and roadmap for KLDiscovery's client-hosting platforms • Lead architecture for major client-platform programs, including Nebula and Relativity hosting modernization, client data migrations at scale, multi-region expansion, and storage architecture for client review and processing workloads • Own cloud and network architecture strategy for client-facing platforms • Provide architectural oversight and review gates for cross-functional programs • Partner with the security organization on tenant isolation, client data protection, audit and compliance posture • Lead, mentor, and develop team, setting technical direction and raising the bar on design quality • Advise senior IT, Product, Client Services, and Sales and Solutions Engineering leaders on technology investments, build-versus-buy decisions