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Vetted seaborn Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in LLM inference and MLOps
Intern Data Scientist specializing in LLMs, RAG, and data engineering
Mid-level Data Scientist specializing in GenAI, NLP, and deep learning
Staff Software Engineer specializing in frontend platforms and healthcare UX
Junior Software Engineer specializing in Trust & Safety, QA automation, and ML
Junior Software Engineer specializing in cloud microservices and monitoring
Intern AI Researcher specializing in NLP, multimodal generative AI, and medical imaging
Mid-level Data Scientist specializing in GenAI, NLP, and deep learning
Intern Machine Learning Engineer specializing in RL post-training for LLMs and VLMs
Mid-level AI/ML Data Engineer specializing in data pipelines, MLOps, and LLM/RAG systems
Mid-level Full-Stack Developer specializing in cloud-native apps, AI/ML, and microservices
Mid-level Robotics & Firmware Engineer specializing in continuum robotics and SerDes validation
Sharath Nyalakonda
Mid-level AI/ML Engineer specializing in NLP, graph models, and MLOps for FinTech and Healthcare
“AI/ML engineer who has deployed production LLM/transformer-based systems for merchant intelligence and fraud/support optimization, delivering +27% merchant engagement and +18% payment success. Deep experience in privacy-preserving, PCI DSS-compliant data/ML pipelines (Airflow, AWS Glue, Spark, Delta Lake) and scalable microservices on Kubernetes, plus proven cross-functional delivery in healthcare claims analytics at UnitedHealth Group (12% HEDIS claim reduction).”
Gagan Mundada
Intern Machine Learning Engineer specializing in multimodal AI and evaluation benchmarks
“ML-focused candidate with beginner ROS/ROS2 experience (custom pub-sub nodes; TurtleBot3 SLAM simulation debugging via topic inspection and transform/orientation checks). Has research/project exposure to LLM training approaches (GRPO with pseudo-labels using Hugging Face TRL on Qwen/Llama) and uses Docker/Kubernetes + CI/CD to run ViT saliency-attention/compression workloads on UCSD Nautilus infrastructure.”
Abhay Murjani
Director-level Data Science Manager specializing in ML forecasting, experimentation, and MLOps
“Data/ML engineer with experience at American Express and Amazon, owning an end-to-end rewards redemption/liability ML pipeline (~200GB) with rigorous regulatory/audit validation and quarterly executive reporting. Also built web-scraped product datasets with anti-bot protections at a startup and helped modernize an authn/authz service using AWS, plus led early-stage migration work from an internal warehouse to GCP with CI/CD and cloud observability.”
Alexander Smith
Junior Software Engineer and Data Scientist specializing in AI/ML systems
“Built production-grade automation and ML/data pipelines at Dun & Bradstreet and ThreadNotion, spanning large-scale document classification, country risk report automation, and resilient Playwright testing for dynamic AI chat workflows. Particularly strong in turning brittle or ambiguous systems into reliable, observable, end-to-end automated platforms.”
Shuju Sun
Mid-Level Software Engineer specializing in real-time data pipelines and ML deployment
“Ticketmaster data engineer who built CDC-driven Kafka pipelines feeding Snowflake for analytics and data science teams. Hands-on in production operations—scaled Kafka during sudden playoff-driven transaction spikes and improved monitoring for preemptive scaling. Known for using small-batch experiments and quantitative metrics to align stakeholders and drive cost-saving architecture changes (e.g., buffering to reduce AWS Lambda invocation frequency).”
Sri Lekha Kandadai
Mid-level Machine Learning Engineer specializing in MLOps, monitoring, and multimodal AI
“ML/AI engineer focused on production-grade model reliability: built a monitoring and validation framework to detect drift, trigger anomaly alerts/retraining, and maintain consistent performance for device intelligence workflows at scale. Strong MLOps background with Python pipelines, Docker/Kubernetes deployments, Airflow orchestration, and real-time monitoring dashboards; experienced partnering with product managers to deliver business-facing insights.”