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Vetted Jupyter Notebook Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in real-time fraud detection and edge AI
Executive Technology Leader (CTO/Principal Engineer) specializing in cloud-native platforms and AI
Mid-level Machine Learning Engineer specializing in MLOps, RAG, and real-time personalization
Mid-Level Backend/Full-Stack Software Developer specializing in Java, AWS, and cloud-native APIs
Senior Data Scientist specializing in AI/Deep Learning and applied machine learning
Mid-level AI/ML Engineer specializing in GenAI agents and production ML systems
Mid-level Machine Learning Engineer specializing in MLOps and Generative AI
Mid-level Machine Learning Engineer specializing in fraud detection and recommendations
Bhagya Sunkara
Mid-level Full-Stack Developer specializing in cloud-native backend services and real-time data platforms
“Backend/data engineering candidate with Netflix experience designing and migrating analytics platforms from batch to real-time streaming (Kafka/Flink) across AWS and GCP. Delivered measurable improvements (40% lower data delay, 99.9% accuracy) using phased rollouts, automated data validation (Great Expectations), and strong observability (Prometheus/Grafana), and proactively hardened pipelines with idempotency to prevent duplicate Kafka processing.”
Kella Dhanush Venkata Sai
Junior ML Engineer specializing in Generative AI and LLM applications
“Built a production internal knowledge assistant using a RAG pipeline over large spreadsheets, PDFs, and support documents, using transformer embeddings stored in FAISS. Focused on real-world production challenges—format normalization, retrieval quality, hallucination reduction (context-only + citations), and latency—using hybrid retrieval, quantization, and containerized deployment, and communicated the workflow to non-technical stakeholders using simple analogies.”
Christopher Khan
Senior Software Engineer specializing in Python, cloud platforms, and distributed systems
“Backend/data engineer with production experience at Walmart and HealthSnap building Python services and data pipelines on AWS (EKS, Lambda, Glue, Airflow). Strong reliability and operations focus—implemented idempotency + circuit breakers for peak-traffic consistency issues, GitOps CI/CD, and observability. Demonstrated measurable performance wins (Postgres p95 45s to <5s, ~60% CPU reduction) and modernized SAS batch workflows to Python with parallel-run parity validation and feature-flagged rollout.”
Mid-level Machine Learning Engineer specializing in deep learning, MLOps, and real-time inference
Intern Product & Analytics professional specializing in SaaS, LegalTech, and Healthcare IT
Mid-Level Backend/Full-Stack Software Developer specializing in cloud-native APIs
Mid-level Solutions Engineer specializing in API integrations and data products
Mid-level Data Scientist specializing in LLMs, RAG, and personalization
Mid-level Data Engineer specializing in AWS ETL and data warehousing
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level Machine Learning Engineer specializing in recommender systems and LLM/RAG pipelines
Staff-level Backend Engineer specializing in distributed data platforms and AI infrastructure
Technology Executive specializing in AI-native engineering and cybersecurity governance