• Execute plant trials, line qualifications, and process validations hands-on • Run Design of Experiments (DOE) and process capability studies at the machine level • Set up and operate injection molding, blow molding, and related equipment during development trials • Build and verify first-article samples, perform dimensional checks using CMM and hand gauges • Troubleshoot tooling, mold, and processing issues during trials • Develop and execute sample and test plans, procure test fixtures and gages • Support customer line trials on-site at customer facilities • Participate in and contribute to DFMEA/PFMEA workshops • Execute assigned tasks within the Stage-Gate process • Support the Product Development Manager in preparing technical documentation

• Execute VMware Enterprise Migrations • Execute end-to-end VMware datacenter migrations using AWS Migration Services (MGN), CloudEndure, and HCX • Perform comprehensive VMware workload assessments, dependency mapping, and application discovery • Implement migration strategies aligned with the 7Rs framework (Rehost, Replatform, Refactor, Repurchase, Retire, Retain, Relocate) • Deploy and operate migration factory patterns for VMware datacenter exits with 200-500 VMs per wave • Execute wave-based migration planning with minimal business disruption and tested rollback procedures • Migrate complex VMware environments including vMotion transitions, NSX networking conversions, and vSAN/VMFS storage migrations • Build Migration Infrastructure • Build AWS landing zones and migration target environments following Well-Architected Framework principles • Implement hybrid connectivity solutions (Direct Connect, VPN, Transit Gateway) for migration networks • Configure AWS migration tools: MGN replication servers, DMS endpoints, Migration Hub tracking • Convert VMware NSX micro-segmentation policies to AWS Security Groups and Network ACLs • Migrate VMware vSAN/VMFS/NFS storage to appropriate AWS storage services (S3, EBS, EFS, FSx) • Implement disaster recovery and business continuity architectures post-migration • Conduct post-migration validation, performance testing, and optimization • Automate Migration Workflows • Develop Infrastructure-as-Code using Terraform and CloudFormation to replicate VMware infrastructure patterns in AWS • Create Python and PowerShell automation scripts for mass VM migration, validation, and cutover procedures • Build CI/CD pipelines for automated testing and deployment of migrated workloads • Automate post-migration tasks: tagging, monitoring setup, backup configuration, security hardening • Implement migration runbooks and standard operating procedures for repeatable execution • Enable Client Success • Provide hands-on technical guidance to client VMware and cloud engineering teams during migration execution • Lead migration workshops, cutover planning sessions, and incident response during maintenance windows • Troubleshoot complex VMware-to-AWS migration challenges (replication failures, networking issues, application dependencies) • Create comprehensive migration documentation: runbooks, architecture diagrams, lessons learned • Support post-migration hypercare and stabilization activities

• Own Primer's internal developer platform end to end: the CI/CD pipelines, deployment workflows (canary, blue/green), self-service tooling and ephemeral environments that every engineer here depends on to ship. • Build the human-AI development loop. This is the most open part of the role: the tooling and automation that let engineers work effectively alongside coding agents, and the patterns that make agent-driven workflows fast and safe at Primer. • Treat developer productivity as a measurable system. Instrument it, find where delivery actually slows down, and ship the changes that fix it instead of guessing. • Take real operational ownership. You'll join the Core Infrastructure on-call rotation and own the reliability of what you build. • Set the technical direction for how Primer's engineers build and ship, and bring less experienced engineers along with you as you do it. • Improve how engineers write code, not just how they ship it. Partner with product teams to make the tools, frameworks and internal APIs they use everyday more ergonomic, and help reduce the friction of building features at Primer. • Build Primers paved roads. Reduce the application boilerplate engineers write, shape standards around how services are built and configured, and own the golden-path tooling that lifts efficiency across every team. • Work in the open across a fully distributed team, making your decisions and trade-offs visible rather than holding them in your head.

Role Description We are seeking a Model Serving Engineer to design, build, and operate high-performance, highly reliable inference platforms for serving large machine learning models in production. The role focuses on the systems engineering side of AI deployment, including: - Request routing - Batching - Caching - Autoscaling - GPU utilization - End-to-end observability across diverse model workloads The ideal candidate brings strong distributed systems and performance engineering expertise, has shipped serving systems at scale, and understands the trade-offs between latency, throughput, cost, and quality in ML serving. Qualifications - Bachelor’s or Master’s degree in Computer Science or a related field - Six or more years of experience in distributed systems, infrastructure, or ML platform engineering - Strong proficiency in Python and a systems language such as Go, Rust, or C++ - Deep experience operating high-throughput, low-latency services in production - Hands-on experience with LLM or large model inference frameworks such as vLLM or TensorRT-LLM - Strong understanding of GPU architecture, memory hierarchies, and accelerator utilization - Familiarity with Kubernetes, autoscaling, and modern cloud platforms - Experience with observability stacks including metrics, tracing, and structured logging - Solid grounding in performance engineering and capacity planning - Strong communication and incident response skills Requirements - Design and operate model serving platforms supporting diverse workloads including LLMs, vision models, and recommendation systems - Optimize inference performance using continuous batching, paged attention, speculative decoding, and request multiplexing - Implement multi-tenant routing, rate limiting, and quality-of-service policies across model endpoints - Build autoscaling and capacity management systems that balance latency, throughput, and cost - Tune GPU utilization, memory management, and KV cache strategies for LLM serving workloads - Integrate model serving with API gateways, identity systems, and observability platforms - Implement caching, prompt deduplication, and response reuse strategies where appropriate - Drive end-to-end observability including latency histograms, queue dynamics, GPU utilization, and error tracking - Develop deployment workflows including canary releases, shadow testing, and automated rollback - Operate incident response for high-availability AI services and drive durable reliability improvements - Collaborate with ML and product teams to support new model releases and capability rollouts - Implement security controls including request signing, content filtering, and abuse detection at the serving layer - Document operational procedures, performance characteristics, and tuning guidance for internal teams - Stay current with AI serving research and translate advances into production capabilities Benefits - Competitive base salary commensurate with experience - Benefits package How to Apply 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 .