Sriram Sridhar
Mountain House,CA
Summary
Accomplished Staff Machine Learning Engineer at Personio Inc., specializing in optimization of generative AI models and leadership of cross-functional teams. Demonstrated success in improving model serving efficiency by 45% through advanced performance profiling and technical guidance, utilizing expertise in Python and MLOps to deliver impactful AI solutions.
Overview
26
26
years of professional experience
Work History
Staff Machine Learning Engineer
Personio
New York, NY
10.2024 - Current
- AI Infrastructure Development: Designed and implemented agentic conversational AI systems reducing inference latency by 40% through custom CUDA kernels and model optimization techniques
- Technical Leadership: Leading 5-person ML engineering team in architecting AI infrastructure roadmap, establishing performance benchmarks, and driving adoption of hardware-accelerated ML solutions across product verticals
- Systems Integration: Built MCP (Model Context Protocol) interfaces for seamless integration between AI services and backend systems, ensuring optimal resource utilization and scalability
- Performance Optimization: Implemented GPU memory optimization strategies and distributed inference pipelines for real-time AI applications
Startup Founder
Algorithmical Corp
, MA
02.2023 - Current
- High-Performance Trading Systems: Developed fully automated day trading platform with microsecond-latency requirements, implementing custom C++ algorithms optimized for real-time market data processing
- ML Infrastructure: Built scalable recommendation engine using distributed computing frameworks, optimizing for both CPU and GPU acceleration
- Systems Architecture: Designed end-to-end ML pipeline with hardware-aware model deployment, achieving 60% improvement in inference throughput
Staff Machine Learning Engineer
Warner Bros. Discovery, Inc
02.2022 - 10.2024
- ML Systems Optimization: Designed and optimized generative AI models and recommendation engines for MAX platform, implementing hardware-specific optimizations that improved model serving efficiency by 45%
- Infrastructure Development: Built and deployed large-scale AI systems on AWS with custom GPU clusters, implementing MLOps practices for continuous model optimization and hardware resource management
- Cross-functional Leadership: Collaborated with hardware engineering teams to optimize AI workloads for specific GPU architectures, translating business requirements into technical specifications for hardware-accelerated solutions
- Performance Engineering: Implemented advanced profiling and optimization techniques for transformer models, achieving significant improvements in training and inference performance
- Technologies: Python, C++, TensorFlow, PyTorch, CUDA, Kubernetes, AWS, Databricks
Senior Software Engineer
Amazon
Boston, MA
08.2019 - 02.2022
- Alexa AI Infrastructure: Led development of hardware-optimized generative AI systems for Alexa platform, implementing efficient memory management and compute optimization for edge devices
- ML Framework Development: Built and optimized NLP models with transformer architectures, focusing on hardware acceleration and real-time performance requirements
- Systems Integration: Deployed large language models on distributed AWS infrastructure with custom optimization for GPU utilization and cost efficiency
- Cross-team Collaboration: Partnered with hardware teams to optimize AI workloads for Amazon's custom silicon, ensuring optimal performance across different device architectures
- Technologies: Python, C++, TensorFlow, PyTorch, AWS, CUDA, Redshift
Principal Member Of Tech Staff
Verizon Labs
Waltham, MA
06.2015 - 08.2019
- AI Systems Development: Developed and deployed AI-powered solutions including chatbots and recommendation systems, with focus on performance optimization for telecom infrastructure
- Big Data Infrastructure: Built and optimized ML pipelines using Spark and Hadoop, implementing distributed computing solutions for large-scale data processing
- MLOps Implementation: Led implementation of MLOps practices ensuring reliability and scalability of AI systems across multiple platforms and hardware configurations
- Hardware Integration: Collaborated with network hardware teams to optimize AI workloads for telecom equipment, focusing on power efficiency and real-time processing requirements
- Technologies: Python, C++, TensorFlow, PyTorch, Spark, Hadoop, Azure
Principal Engineer
Telecom Systems Engineering Experience
01.2000 - 01.2015
- High-Performance Systems: Extensive experience in C/C++ development for telecom infrastructure, including real-time signal processing, network protocol implementation, and embedded systems optimization
- Hardware-Software Integration: Deep expertise in SW/HW co-design for telecom equipment, optimizing software for specific hardware architectures and performance constraints
- Distributed Systems: Built scalable, fault-tolerant systems for telecom networks with focus on low-latency, high-throughput requirements
- Performance Optimization: Advanced profiling and optimization techniques for resource-constrained environments, including memory management and computational efficiency
- Various Companies | 15 Years Combined Experience
Education
Master of Science - Data Science
University of Michigan
Ann Arbor, MI12.2025
Master of Science - Entrepreneurship
University of Massachusetts
12.2003
Master of Science - Software Engineering
Illinois Institute of Technology
Chicago, IL09.1999
Bachelor of Science - Electrical & Electronics
National Institute of Technology
Suratkal, India09.1997
Skills
- ML Systems & Infrastructure: Distributed AI systems, performance profiling and optimization
- AI Frameworks & Tools: PyTorch, TensorFlow, CUDA, OpenMP, MPI, Kubernetes, Docker, AWS, MLOps pipeline development
- Programming & Systems: Advanced C/C (15 years), Python, CUDA programming, parallel computing, high-performance computing, embedded systems optimization
- ML Domains: Generative AI, NLP, recommendation systems, ranking models, transformer architectures, model quantization and optimization
- Leadership: Technical team leadership, cross-functional collaboration, mentorship, project management, AI strategy development
Leadership & Mentorship
- Technical Mentorship: Actively mentor junior engineers and research scientists, focusing on ML systems design, performance optimization, and career development
- Cross-functional Leadership: Experience leading large-scale projects across multiple teams, driving technical decisions and ensuring alignment with business objectives
- Quality Engineering: Established engineering best practices and code review processes that improved overall team productivity and system reliability
Timeline
Staff Machine Learning Engineer
Personio
10.2024 - Current
Startup Founder
Algorithmical Corp
02.2023 - Current
Staff Machine Learning Engineer
Warner Bros. Discovery, Inc
02.2022 - 10.2024
Senior Software Engineer
Amazon
08.2019 - 02.2022
Principal Member Of Tech Staff
Verizon Labs
06.2015 - 08.2019
Principal Engineer
Telecom Systems Engineering Experience
01.2000 - 01.2015
Master of Science - Data Science
University of Michigan
Master of Science - Entrepreneurship
University of Massachusetts
Master of Science - Software Engineering
Illinois Institute of Technology
Bachelor of Science - Electrical & Electronics
National Institute of Technology
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