Senior AI/ML Infrastructure Engineer

Chris Cisneros

Senior Software Engineer

*****.***.***@*****.*** 951-***-**** San Francisco, CA www.linkedin.com/in/chris-c-0a793a90 SUMMARY

Senior Software Engineer with 10+ years of experience specializing in designing, developing, and deploying scalable AI/ML systems, NLP applications, and production-grade machine learning infrastructure. Expertise in Python, TensorFlow, PyTorch, and cloud platforms (AWS SageMaker, GCP AI Platform) with a proven track record of building and optimizing models for performance, reliability, and business impact across healthcare, mobility, e-commerce, and enterprise domains. Demonstrated success in leading cross-functional initiatives to integrate AI capabilities into distributed platforms, modernize ML workflows, and deliver measurable improvements in model accuracy, system latency, and operational efficiency.

SKILLS

- AI/ML & GenAI: TensorFlow, PyTorch, Keras, LLMs, NLP, Computer Vision, Model Optimization, Statistical Modeling, ML Algorithms, RAG Architectures, AI Agents, Prompt Engineering, ML Infrastructure

- Programming Languages: Python, TypeScript, Node.js, Go, SQL, Bash

- Cloud AI Platforms & Infrastructure: AWS SageMaker, Google Cloud AI Platform, AWS Lambda, Azure ML, Kubernetes, Docker, CI/CD, Terraform

- Data Engineering & Processing: Apache Spark, Apache Kafka, PostgreSQL, Real-Time Pipelines, Batch Processing, Feature Engineering, Vector Databases

- System Design & Architecture: Distributed Systems, Microservices, Scalability, Reliability Engineering, API Design, Model Deployment

- Tools & Collaboration: Git, Jira, Confluence, Agile Development, Cross-Functional Collaboration PROFESSIONAL EXPERIENCE

Accenture San Francisco, CA

Senior Software Engineer May 2023 – Present

- Designed and deployed enterprise-scale AI and machine learning models using TensorFlow and PyTorch to automate business workflows, integrating with cloud platforms like AWS SageMaker for managed training and inference.

- Architected NLP-powered RAG systems leveraging vector databases and transformer models to process enterprise documents, improving question-answering accuracy by 40% for internal knowledge retrieval.

- Developed computer vision models for document parsing and classification within client intake systems, reducing manual data entry and improving processing throughput by 30%.

- Optimized AI model performance and latency by implementing quantization, model pruning, and efficient serving patterns on Kubernetes, achieving a 50% reduction in inference cost for high-volume prediction services.

- Built a scalable ML feature platform using Apache Spark and Kafka to serve real-time features for fraud detection and recommendation models, improving feature freshness and model accuracy.

- Engineered CI/CD pipelines for ML models incorporating automated testing, versioning with MLflow, and canary deployments on AWS, increasing deployment frequency and reducing rollout failures.

- Led cross-functional collaboration with data scientists and product teams to translate prototype models into production- grade services, establishing model monitoring, drift detection, and retraining workflows.

- Modernized legacy reporting systems by integrating NLP models for sentiment analysis and topic modeling, providing actionable insights and reducing manual analysis effort by 60%.

- Implemented A/B testing frameworks and performance dashboards to measure model impact on key business metrics, enabling data-driven iteration on AI initiatives.

- Mentored junior engineers on ML system design, cloud AI services, and model optimization best practices, raising team capability in delivering robust AI solutions. Uber San Francisco, CA

Senior Software Engineer Jul 2019 – Apr 2023

- Designed and scaled the machine learning platform (Michelangelo) infrastructure, developing TensorFlow and PyTorch based training pipelines for marketplace forecasting, pricing, and ETA models.

- Built high-throughput NLP models for rider/driver support ticket classification and intent recognition, improving automated routing accuracy and reducing manual review load by 35%.

- Engineered real-time feature pipelines using Apache Kafka and Spark Streaming to serve low-latency features for online ML models, critical for dynamic pricing and fraud detection systems.

- Optimized distributed model training workloads on Kubernetes, implementing GPU orchestration and spot instance strategies that reduced training costs by 25% while improving cluster utilization.

- Developed computer vision components for map data processing and signage recognition, contributing to improved map accuracy and navigation features within the mobility platform.

- Created model serving infrastructure with performance monitoring, automatic scaling, and canary deployment capabilities, increasing inference service reliability to 99.9% uptime.

- Collaborated with ML research teams to productionize novel algorithms, building robust data validation, model evaluation, and performance tracking systems.

- Implemented statistical modeling and anomaly detection for monitoring platform health, identifying operational issues proactively and reducing mean time to detection (MTTD).

- Led initiatives to improve ML developer velocity by building reusable libraries for feature encoding, model serialization, and experiment tracking, reducing time-to-production for new models. Amazon Seattle, WA

Software Engineer Feb 2018 – Jun 2019

- Developed machine learning microservices for transaction risk scoring and recommendation systems, utilizing TensorFlow and AWS SageMaker for model training and deployment.

- Built NLP components for customer review analysis and product categorization, integrating with core e-commerce services to enhance search and discovery experiences.

- Engineered batch and real-time data pipelines using Apache Spark to preprocess training datasets and generate features for personalization and forecasting models.

- Optimized model inference latency by implementing efficient serving patterns with TensorFlow Serving and caching strategies, reducing P95 latency by 40% for high-traffic services.

- Implemented model performance monitoring and alerting using CloudWatch and custom dashboards, ensuring consistent model quality and triggering retraining upon drift detection.

- Contributed to ML platform tooling that simplified model deployment and A/B testing for product teams, accelerating the iteration cycle on AI-driven features.

- Participated in on-call rotations for ML serving infrastructure, diagnosing performance issues and implementing fixes to maintain system reliability and uptime SLAs.

Oscar Health Los Angeles, CA

Backend Engineer Aug 2014 – Jan 2018

EDUCATION

Coleman University San Diego, CA

Bachelor’s degree, Computer Science 2010 – 2014

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