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Vetted Jupyter Notebook Professionals
Pre-screened and vetted.
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
Mid-level AI/ML Engineer specializing in LLMs, MLOps, and recommendation systems
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
Senior Machine Learning Engineer specializing in GenAI, NLP, and MLOps
Technology Executive specializing in AI-native engineering and cybersecurity governance
Peeyush Dyavarashetty
Intern AI/ML Engineer specializing in GenAI, LLMs, and agentic RAG systems
“AI/LLM practitioner who built a GPT-2-like language model from scratch at the University of Maryland using PyTorch and multi-GPU distributed training, with experiment tracking in Weights & Biases. As an AI Operations intern at ScaleUp360, delivered multiple production-style AI agent automations (Gmail classification and Fireflies-to-Claude workflows that extract and assign CEO tasks) and set up measurable evaluation using test cases and classification metrics.”
Pavan Devulapalle
Mid-level Software Engineer specializing in cloud platforms and AI-integrated full-stack development
“Backend engineer who built Flask-based internal APIs supporting GenAI-driven provisioning/diagnostics (Outpost/AWS Outposts-like environment), with deep hands-on optimization across Postgres/SQLAlchemy (2s to <200ms endpoint improvement). Experienced integrating ML/LLM workflows via AWS SageMaker and Bedrock, and designing multi-tenant isolation plus high-throughput Redis-backed background task pipelines (minutes to seconds).”
winston lo
Junior Software Engineer specializing in AI agents, RAG, and full-stack development
“Backend engineer who built and iterated a secure, multi-tenant RAG system over a large document corpus, emphasizing strict RBAC/ACL isolation, hybrid retrieval (vector+keyword), reranking, and strong observability to balance relevance, latency, and cost. Also led production refactors/migrations using strangler + feature flags/dual writes and has experience catching subtle real-world failure modes (including in a sensor calibration optimization pipeline).”
Narasimha Jwalapuram
Mid-level Applied AI Engineer specializing in LLM agents, RAG, and model alignment
“Applied Scientist with legal-tech experience who builds production LLM systems. Created and deployed Quibo AI, a LangGraph-based multi-agent pipeline that turns large markdown/Jupyter inputs into polished blogs and social posts, overcoming context limits via ChromaDB + HyDE RAG. Also built a large-scale iterative code-evolution workflow using multi-model orchestration (GPT/Claude/Gemini) with testing, debugging loops, and evaluation/observability practices.”