Role Description We are seeking a Machine Learning Infrastructure 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 salary range: $100,000–$150,000 Annually - Full-time, Direct W2 position - 100% Remote work opportunity Company Description Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.

• Collaborate with cross-functional partners (Product Managers, Data Scientists, Data Engineers, Software Engineers, Business teams) to build data and Machine Learning products • Take ownership of objectives and key results for your workstream, and own technical solutions in partnership with your manager • Architect and build robust systems to train, deploy, run inference, and monitor Machine Learning and AI systems at scale • Champion code quality, reusability, scalability, maintainability, and security, and provide input into strategic architecture decisions • Implement processes and tools to ensure data quality, enforce data governance policies and engineering best practices • Integrate Machine Learning and AI systems with production applications • Innovate with new approaches, staying abreast of current research and latest technologies in the broader ML engineering community

Role Description Next-Generation Infotainment System project for German premium carmaker at Luxoft Automotive Division. There is a wide range of applications provided by domains for the Head Unit, such as Tuner, Player, etc. The project life-cycle covers all stages from design to development and delivery to the client. The project team works in tight collaboration with one of the premium OEMs in the world and is located in the Southeast Asia region. You will have the opportunity to be part of the team that is developing functionality for the most technologically advanced cars in the world. - Develop and maintain Automotive HMI applications using Qt/QML and C++ - Implement UI features and integrate QML frontend with C++ backend - Collaborate with Architects, Developers, Test Engineers, and Product Owners to deliver high-quality software - Participate in design discussions, code reviews, estimation, and troubleshooting activities - Contribute to software quality, performance optimization, and continuous improvement initiatives Qualifications - Upper Intermediate level of English - 4+ years of software engineering experience - 2+ years of Qt/QML development experience - 1+ year of Automotive project experience (IVI, Cluster, Cockpit, or HMI) - Strong C++11/14 knowledge - Strong Qt Framework and QML knowledge - Experience with Embedded Linux development - Experience with Git and code review processes - Good understanding of object-oriented design principles Requirements - Experience working in an Agile/Scrum environment - Knowledge of automotive communication protocols (CAN, SOME/IP) - Experience with Android Automotive OS (AAOS) - Knowledge of CommonAPI and/or FRANCA - Understanding of ASPICE processes - Excellent interpersonal skills and high motivation Company Description

Role Description - Design and implement scalable data processing pipelines for ML training and validation - Build and maintain feature stores with support for both batch and real-time features - Develop data quality monitoring, validation, and testing frameworks - Create systems for dataset versioning, lineage tracking, and reproducibility - Implement automated data documentation and discovery tools - Design efficient data storage and access patterns for ML workloads - Partner with data scientists to optimize data preparation workflows Qualifications - 7+ years of software engineering experience, with 3+ years in data infrastructure - Strong expertise in GCP's data and ML infrastructure: - BigQuery for data warehousing - Dataflow for data processing - Cloud Storage for data lakes - Vertex AI Feature Store - Cloud Composer (managed Airflow) - Dataproc for Spark workloads - Deep expertise in data processing frameworks (Spark, Beam, Flink) - Experience with feature stores (Feast, Tecton) and data versioning tools - Proficiency in Python and SQL - Experience with data quality and testing frameworks - Knowledge of data pipeline orchestration (Airflow, Dagster) Requirements - Experience with streaming systems (Kafka, Kinesis) - Experience with GCP-specific security and IAM best practices - Knowledge of Cloud Logging and Cloud Monitoring for data pipelines - Familiarity with Cloud Build and Cloud Deploy for CI/CD - Experience with streaming systems (Pub/Sub, Dataflow) - Knowledge of ML metadata management systems - Familiarity with data governance and security requirements - Experience with dbt or similar data transformation tools