Vetted Data Ingestion Professionals

“Software engineer with hands-on experience building data-intensive and 3D-processing web applications (React/Next.js/TypeScript + Node.js). Has worked in microservices using RabbitMQ for event-driven workflows and built an internal ops/engineering dashboard to monitor pipeline jobs, surface logs, and manage retries—improving visibility and reducing on-call/debug time.”

“Built across cloud infrastructure, AI-powered product workflows, and backend data reliability in environments including Northeastern, Knead, and Grafx. Particularly compelling for roles needing someone who can both ship AWS-based systems end-to-end and debug messy production issues involving caching, APIs, and data pipelines.”

“Backend/platform engineer with payments and insurance domain experience (Cognizant), owning high-volume production systems end-to-end. Shipped a Spring Boot payment tokenization service with strong observability and phased migration that cut transaction latency ~30% and improved payment efficiency ~25%. Also productionized an ML-driven financial health/risk analytics pipeline with near real-time dashboards across 70+ schools, emphasizing interpretability, data quality, and drift monitoring.”

“Robotics software contributor on an SJSU Robotics Mars rover hub, where they built a C++ camera gimbal driver using the Libhal open-source library and implemented/tuned PI/PID control to achieve stable servo behavior.”

“Built and shipped a production LLM-powered decision system that replaced a slow, inconsistent manual review process by turning messy text into structured, auditable outputs behind an API. Demonstrates strong end-to-end ownership of reliability and operations (schema validation, retries/fallbacks, latency/cost controls, monitoring for drift) and a disciplined approach to evaluation and regression testing. Experienced collaborating with non-technical reviewers to define success criteria and deliver interpretable outputs that get adopted.”

Isha Harne

Screened

Intern Software Engineer specializing in ML applications and LLM platform engineering

New York, NY1y exp

“Full-stack engineer who builds and scales customer-facing and internal AI products end-to-end (React/TypeScript/FastAPI/MongoDB) with strong product instrumentation and rapid MVP iteration. Built an AI-powered code review assistant adopted across teams and integrated into CI/CD, reducing manual review time by 30%+, and has hands-on experience with LLM retrieval/reasoning systems (LangChain + FAISS) and microservices scaling using RabbitMQ, Docker, and AWS.”

Sakethram Marpu

Screened

Junior Investment Analyst specializing in AI & DeepTech

Bengaluru, India2y exp

“VC-style founder sourcer who uses technical signals (GitHub) and niche communities (Elpha/Indie Hackers/Discord) to identify early-stage opportunities, including thesis-driven sourcing in applied AI infrastructure/observability from YC W24. Emphasizes value-first LinkedIn outreach and long-horizon relationship building (e.g., built a personal relationship with Snitch’s CTO who later reached out first about a new startup).”

“Hands-on backend engineer with practical experience improving latency in Django-based API systems by fixing missing indexes and eliminating N+1 queries. Also built an AI scheduling system using FastAPI, a relational database, AI/ML workflows, and an operational reporting dashboard, with a clear bias toward correctness and maintainable architecture.”

“ML/AI engineer with strong healthcare insurance domain depth who has owned fraud detection and LLM claims products end-to-end in production. Stands out for combining modern MLOps and RAG architecture with measurable business impact, including millions in fraud savings, 40% faster analysis, and reusable platform tooling that accelerated multiple teams.”

Sasi Kanduri

Screened

Senior Full-Stack Software Engineer specializing in backend systems and workflow platforms

Sacramento, CA7y exp

“Full-stack engineer with strong React and Python backend depth who has owned complex analytical products end-to-end, from performant UIs to FastAPI services, SQLAlchemy data models, Redis caching, and production observability. Particularly compelling is their 0→1 automation work in the water systems domain, where they built Airflow- and LLM-powered workflows that reduced manual notification and correction work by 90%.”

Sri Mounika Jammalamadaka

Screened

Mid-level AI/ML Engineer specializing in GenAI, LLMs, and data platforms

Fairfax, VA6y exp

“Built and helped deploy a production RAG-based LLM assistant for HVAC anomaly diagnostics, partnering closely with field engineers and operations teams to make AI outputs trustworthy in real workflows. Stands out for practical post-launch optimization work—improving retrieval quality, reducing hallucinations, and stabilizing non-deterministic behavior—which contributed to roughly a 40% reduction in diagnosis time.”

“AI Engineer at Humanitarian AI who has built and productionized both a LangGraph-based multi-agent workflow system and a RAG pipeline (OpenAI embeddings + vector DB) with rigorous evaluation/guardrails. Reports strong measurable impact (60% faster workflow delivery, 40% fewer incidents, 70% reduced research time) and has prior enterprise modernization experience at Infosys migrating ETL to microservices with zero production incidents.”

“Built and productionized a clinical NLP + patient risk stratification platform at Dermanture, combining Spark/PySpark pipelines with BERT/BioBERT for entity extraction and text classification and downstream risk models in TensorFlow/scikit-learn. Experienced running regulated, auditable ML workflows with Airflow and AWS SageMaker, emphasizing data validation (Great Expectations), drift monitoring, and explainability (SHAP) to drive clinician trust and adoption.”

“Accenture engineer who built and shipped a production RAG-based automation/chatbot for SAP incident triage and troubleshooting, embedding thousands of runbooks/logs/tickets into a semantic search pipeline and integrating it into Teams/Slack. Reported major productivity gains (30–60% time reduction), >90% validated answer accuracy, and sub-2-second responses, with strong orchestration (Airflow/Prefect/LangGraph) and reliability practices (guardrails, testing, monitoring).”

Sam Fish

Screened

Senior Full-Stack Engineer specializing in web platforms, cloud infrastructure, and data systems

Culver City, CA5y exp

“Full-stack/product-leaning engineer who owned an end-to-end AI Tutor feature (Claude-powered) shipped simultaneously to iOS/Android/web via Expo, with Cloudflare Workers backend and PostHog analytics. Built the company’s GitHub-based CI/CD to coordinate app store releases with backend blue/green deployments. Also has significant data engineering experience (including ~8TB/day workloads) using dbt/Fivetran plus sharding and hashing-based diffing for correctness.”

prashanth Jamalapurapu

Screened

Mid-level AI/ML Engineer specializing in data engineering, LLM/RAG pipelines, and recommender systems

5y exp

“Research assistant at St. Louis University who built and deployed a production document-intelligence RAG system (Python/TensorFlow, vector DB, FastAPI) on AWS, focusing on grounding to reduce hallucinations and latency optimization via caching/async/batching. Also developed a personalized recommendation system for the Frenzy social platform and partnered closely with product/UX to define metrics and iterate on hybrid recommenders and cold-start handling.”