Sribharath Kainkaryam

Machine Learning and Data Engineering for commodity trading
Developed a distributed and fault-tolerant scraper framework for gathering data from various sources, optimized resource utilization
Designed and deployed processes to ensure data reliability by setting and checking data quality standards, ensuring significant error reduction in data processes
Leading the design for cloud based integrated data analytics platform for firmwide usage, catering to data engineers and analysts
Technologies used: Apache Airflow, Kubernetes, Docker, Terraform

• Developed a distributed and fault-tolerant scraper framework for gathering data from various sources, optimized
resource utilization
• Designed and deployed processes to ensure data reliability by setting and checking data quality standards, ensuring
signficant error reduction in our data processes .
• Leading the design for cloud based integrated data analytics platform for firmwide usage, catering to data engineers
and analysts
• Technologies used: Apache Airflow, Kubernetes, Docker, Terraform

Machine Learning and High Performance Computing for energy prospecting
Incubated, led development and open-sourcing of mdio, a cloud native, scalable storage engine for various types of energy data
Drove growth and commercialization of a SaaS solution for data management
Led to savings of ∼$x0 MM per year in seismic data storage cost for hundreds of petabytes
Grew the data science team from first hire to scaling it by 8x in four years, driving strategic initiatives, and subsequently promoted to team lead role
Collaborated with HPC to design and implement a cost-aware hybrid (on-prem + cloud) high-performance computing roadmap, while establishing robust software engineering practices
Provided technical evaluation of vendor proposals and worked with vendors to build components of data engineering infrastructure
Co-authored a book on application of machine learning in oil and gas
Designed and developed scalable microservices for data streaming and inference
Key technologies used: FastAPI, gRPC, Protocol Buffers, Flask, AWS API Gateway
Developed elastic, fault tolerant, distributed computing infrastructure for scaling up training and inference of deep neural networks on terabytes of seismic data
Key technologies used: PyTorch, torchelastic, Kubernetes, Docker, AWS SageMaker, Vertex AI, MLFlow
Lead for research and development of SaltNet, a workflow for interpreting salt body for velocity model building in seismic imaging
Key technologies used: PyTorch, Dask, TensorRT, Docker
Formulated the problem and designed the dataset for TGS Kaggle salt identification challenge, subsurface exploration industry’s first open data challenge on Kaggle
Co-authored several conference abstracts and journal papers, awarded best paper in The Leading Edge, a premier exploration geophysics journal for 2020

Research and high-performance software development for massively parallel seismic applications
Identified and proposed solutions to workflow inefficiencies in velocity model building workflows costing $1.5 million per year
Lead developer for TraceRay – Schlumberger’s proprietary object-oriented ray tracing library (∼150,000 lines of code) – designed APIs, consulted with users of the library and provided technical support
Achieved a 20% improvement in computational efficiency of TraceRay by redesigning core algorithms and numerical methods for ray tracing

SaaS product for radiation oncology
Mentor on using Apache Airflow for automating data pipelines, improving efficiency in tasks like data preprocessing and model training
Guide integration of MONAI for tasks such as image segmentation, enhancing accuracy in organ and tumour contouring and treatment planning
Advise on data annotation techniques and scalable model deployment, improving accessibility of AI solutions in radiation oncology
Technologies used: MONAI, MONAILabel, NVIDIA Triton