Role Description We are seeking a MLOps 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 - 100% Remote (U.S.) - Full-time, Direct W2 - Salary Range: $100,000–$150,000 Annually - Sponsorship for U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates. 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 .

• Applying Data Science and Machine Learning best practices to develop robust models and support data-driven decision-making across business domains • Applying machine learning and data science techniques such as forecasting, predictive modeling, classification, regression, recommendation, and optimization to solve business problems • Conducting experiments and evaluating models using appropriate statistical, technical, and business performance metrics • Architecting, building, deploying, and maintaining scalable machine learning models and AI solutions integrated into enterprise systems, applications, and operational workflows • Designing and implementing end-to-end ML workflows, including data preparation, feature engineering, model training, validation, deployment, optimization, and continuous monitoring in a high-scale production environment • Developing reusable machine learning components, feature pipelines, and model-serving frameworks to support multiple use cases and teams • Designing and implementing production-grade MLOps solutions using Azure ML, Databricks, MLflow, and related cloud technologies • Building and maintaining automated ML pipelines, feature engineering workflows, feature store patterns, and deployment processes for training, testing, monitoring, and retraining machine learning models • Implementing standards and best practices for model versioning, lifecycle management, governance, deployment automation, model performance monitoring, drift detection, data quality, operational health, and retraining triggers • Developing production-quality Python code, APIs, automation workflows, and machine learning services to integrate ML capabilities into business applications and processes

Role Description We are seeking an AI Research Engineer to bridge cutting-edge applied research and production engineering, designing and shipping advanced machine learning systems that solve high-impact business problems. The role blends scientific rigor with practical software engineering, requiring deep understanding of modern ML and deep learning techniques alongside the ability to build robust, scalable, and well-instrumented production pipelines. The ideal candidate stays current with the rapidly evolving AI research landscape, can critically evaluate new techniques for real-world applicability, and is comfortable operating across the full lifecycle from problem framing and experimentation to deployment and continuous improvement. Key Responsibilities - Design, prototype, and evaluate applied AI solutions across natural language, vision, recommendation, and structured data domains. - Translate ambiguous business problems into well-scoped ML formulations with clear success metrics and evaluation strategies. - Stay current with the latest research in deep learning, large language models, and adjacent areas, and assess applicability to internal use cases. - Implement rigorous experimentation workflows including baselines, ablations, and statistically sound evaluation methodology. - Build production-quality training and inference pipelines using modern ML frameworks and orchestration tools. - Collaborate with ML platform engineers to ensure efficient use of compute, storage, and accelerator resources. - Optimize models for accuracy, latency, throughput, and cost based on production requirements. - Develop tooling for dataset construction, labeling, validation, and ongoing monitoring of data quality. - Partner with product, design, and domain experts to ensure model behavior aligns with user needs and policy requirements. - Implement safety, fairness, and reliability evaluations and incorporate findings into model selection decisions. - Document research findings, design decisions, and operational characteristics clearly for both technical and non-technical audiences. - Mentor engineers on applied ML methodology, evaluation rigor, and responsible deployment. - Contribute to internal knowledge sharing, reading groups, and prototype-to-production playbooks. - Influence the broader AI roadmap based on research insight, capability gaps, and emerging opportunities. Qualifications - Master’s or PhD in Computer Science, Machine Learning, Statistics, or a closely related field; or equivalent applied experience. - Six or more years of combined research and applied ML engineering experience. - Strong proficiency in Python and modern ML frameworks such as PyTorch or JAX. - Hands-on experience training, fine-tuning, and evaluating deep learning models at non-trivial scale. - Solid grounding in mathematics, statistics, and the theoretical foundations of modern ML. - Experience taking ML models from research prototype to production with appropriate observability and safeguards. - Familiarity with distributed training, mixed-precision training, and accelerator hardware. - Strong written and verbal communication skills, including ability to explain complex methods clearly. - Demonstrated ability to read, evaluate, and adapt techniques from current research literature. - Track record of shipping impactful applied AI projects. Preferred Qualifications - Published research at top-tier AI/ML venues. - Experience with large language model training, fine-tuning, or evaluation. - Familiarity with retrieval-augmented generation, agentic systems, or multimodal architectures. - Exposure to responsible AI, model evaluation, and alignment practices. - Experience contributing to open-source ML projects. 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 . Equal Employment Opportunity (EEO) Statement Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall. BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.

Role Description We are seeking a Machine Learning Data 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 - 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 a clear understanding of how data infrastructure choices propagate into model quality and training efficiency. 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 Requirements - 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 Benefits - 100% Remote (U.S.) - Full-time, Direct W2 - Salary Range: $100,000–$150,000 Annually How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] . Equal Employment Opportunity (EEO) Statement Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall. BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.