Vetted Data Processing Professionals

“ML/AI engineer with hands-on ownership of production recommendation and GenAI systems, spanning experimentation, deployment, monitoring, and iteration. Stands out for delivering measurable outcomes—22% CTR lift, 15% conversion lift, and a 30% reduction in support tickets—while demonstrating strong judgment on latency, cost, and safety tradeoffs in real-world systems.”

“Full-stack engineer with strong Next.js App Router/TypeScript experience who built production dataset search/filtering and data-heavy dashboards backed by Postgres. Demonstrates hands-on performance work across the stack (EXPLAIN ANALYZE, composite indexes, caching, React profiling/memoization) and has built durable, Temporal-like orchestrated data-processing workflows with idempotency and retry strategies in an early-stage startup environment (Gaia AI).”

“Backend/platform engineer in healthcare and consulting (Molina Healthcare, TCS) who productionized real-time eligibility/authorization and care navigation workflows with strong reliability and HIPAA security. Demonstrated measurable performance gains (≈40% latency reduction, ~99% uptime) using Spring Boot APIs, SQS decoupling, Redis caching, and deep observability, and regularly leads technical demos that accelerate client adoption.”

“Built and deployed a production enterprise LLM-powered RAG assistant for the construction domain, enabling natural-language querying across PDFs/reports and structured sources (SQL/CSV). Implemented an agent-based routing and multi-agent orchestration approach (LangChain/LangGraph) to reduce hallucinations, improve latency, and deliver actionable, structured responses based on stakeholder feedback.”

Rahul Ganesan

Screened

Intern AI Engineer specializing in LLM systems, RAG, and cloud data pipelines

Washington, PA0y exp

“Built and deployed a production Dockerized multimodal (voice+text) LLM agent for knowledge management that retrieves from Notion and documents and falls back to Tavily-powered web search with citations when internal notes are missing. Emphasizes production reliability via model-switching fallbacks, caching, strict structured outputs (Pydantic/JSON schema), and MCP-based orchestration with state-aware gating and monitoring to reduce redundant tool calls and improve success rates.”

“ML/AI engineer with hands-on ownership of fraud detection and investigator-assist systems, combining anomaly detection with RAG-based LLM summarization in production. Stands out for translating research ideas into reliable cloud-deployed workflows that improved precision to 92%, cut review time by 25-30%, and increased investigator throughput by roughly 30% while also building reusable Python infrastructure for team-wide velocity.”

“Data engineering/software development candidate who built a stock market pipeline and uses that project to demonstrate strong architectural thinking across Kafka, Spark, and Airflow. They stand out for a pragmatic approach to AI: using tools like Copilot, ChatGPT, LangChain, and AutoGen to accelerate development while maintaining human oversight, testing, and system-level decision making.”

Dolly Shyam

Screened

Mid-level Software Engineer specializing in backend systems and AI-driven platforms

New York, NY4y exp

“Backend-focused developer with primary experience in Python, Node.js, databases, and API development. Served as the sole backend engineer on a customer dashboard project, owning database review, API endpoint creation, and coordination with frontend developers for integration.”

SREYAS GANGJI

Screened

Mid-level Software Engineer specializing in AI/ML backend systems

Chicago, IL4y exp

“AI/data engineer at ZS Associates focused on production-grade agentic systems, FastAPI microservices, and cloud-native ETL/RAG pipelines at significant scale. They’ve built multi-agent validation and diagnostic workflows inspired by their Copilot/KUBEPILOT AI work, supporting 500K+ records per day while improving ML inference performance by ~30% and cutting manual troubleshooting by 60%.”

“LLM-focused engineer with end-to-end experience shipping an OpenAI-powered edtech teacher assistant into production, using Humanloop-driven prompt iteration, rigorous observability (Datadog), and A/B testing tied to real learning metrics (25% comprehension lift). Also led adoption-driving technical demos at SiriusXM (event-driven AWS Lambda/Kotlin/CDK pipeline cutting processing from 24 hours to seconds) and partnered with sales at Spresso.ai to close eCommerce SDK deals and boost activation from 40% to 85%.”

Tyler Stroud

Screened

Executive Engineering Leader specializing in AdTech and scalable cloud platforms

Miami, FL15y exp

“Engineering leader with experience in small, bootstrapped startups and exposure to VC environments, currently pursuing CTO-level opportunities. Thrives in fast-iterating, high-uncertainty settings and emphasizes data-driven clarity plus strong problem/market validation when evaluating new ventures.”

“Backend-focused engineer with hands-on experience deploying AI-driven document processing and RAG-based workflows using Python, LangChain, FAISS, and REST APIs. Has owned projects from requirements through post-launch monitoring, including debugging production retrieval issues and building reliable pipelines for messy PDFs/scans and compliance-oriented document analysis.”

“Full-stack engineer with startup-style ownership across backend, frontend, and AI systems, spanning Java/Spring, React, Node/TypeScript, and LLM-powered retrieval. Shipped a workspace intelligence layer using LangChain, OpenAI, and Pinecone to paying customers, while also improving core product metrics like workspace creation success (+30%), latency (450ms to 280ms), and deployment cycle time (-40%).”

“Senior hands-on full-stack engineer with recent Python/React/TypeScript experience at Terakeet and prior Go-focused backend work at Checkly. Stands out for building and scaling analytics and monitoring platforms in startup-style environments, with strong depth in PostgreSQL performance, containerized deployments, and turning ambiguous business goals into reliable production systems.”

Ponugoti Sushma

Screened

Mid-level Machine Learning Engineer specializing in IoT, edge AI, and enterprise ML

Texas, USA5y exp

“Built and productionized an LLM/RAG question-answering service over technical documentation, focusing on retrieval quality (reranking + IR metrics), latency, and scaling. Experienced orchestrating end-to-end ETL/ML workflows with Airflow/Prefect/AWS Step Functions and improving reliability via parallelism, retries, and shadow testing. Also delivered an explainable healthcare risk-flagging classifier with a stakeholder-friendly dashboard for a non-technical program manager.”

Serge Ahranovich

Screened

Executive CTO / Platform Architect specializing in IoT, telematics, and EV charging infrastructure

Los Angeles, CA20y exp

“Founder of TimeTick (timetick.io), an AI-powered diagnostics platform for IoT combining device simulation, automated testing, and real-time monitoring—initially focused on EV charger diagnostics. Former VP of Engineering with a track record of building IoT systems from scratch and applying AI to detect protocol-failure patterns that drive downtime; currently supporting existing customers and converting pilots (with leads like Siemens and ABB) into paid subscriptions.”

“Built and shipped a production LLM-driven data harmonization/record-matching pipeline for pharmaceutical datasets, combining normalization, embeddings/vector search, and an LLM validation step. Emphasizes production reliability via guardrails, confidence thresholds, idempotent/retryable stages, and human-in-the-loop fallbacks, with monitoring focused on manual review and error rates to reduce false positives.”

Mihir Pednekar

Screened

Junior Software Engineer specializing in backend systems and communication infrastructure

Mumbai, India2y exp

“Master’s-level software engineer with hands-on full-stack experience building a React-based task management product and research-driven automation projects in freight logistics. Particularly interesting for roles blending product engineering, data workflows, and early AI/automation systems, with evidence of turning complex analytical outputs into usable visual reports for non-technical audiences.”

“Robotics software engineer leading a GNSS localization effort that fuses GPS, wheel encoders, and camera data via a Kalman filter with robust sensor rejection. Has built ROS/ROS 2 packages (including GPS waypoint following and obstacle avoidance) and has field-tuned motion planning for an autonomous robot operating around penguins in Antarctica, plus handled Docker deployment on NVIDIA Jetson (ARM) systems.”

“Data engineer with healthcare/clinical trial domain experience who owned a 100TB+/month AWS pipeline end-to-end (Glue/S3/Redshift/Airflow) and drove measurable outcomes (20% lower latency, 99.9% reliability, 40% less manual reporting). Also built production data services and API-based ingestion on GCP (Cloud Run/Functions/BigQuery) with strong validation, versioning, and safe migration practices, and launched an early-stage RAG solution (LangChain + GPT-4) for researchers.”

“Experienced contractor-side capture and program leader with a decade supporting a drone program and navigating highly structured government and prime-contractor funding environments. Brings a disciplined, compliance-heavy approach to business development and is especially drawn to smaller, mission-focused companies where the purpose is clearly defined.”

TEJASWI GUDIMETLA

Screened

Mid-level Software Engineer specializing in backend systems, microservices, and AI pipelines

North Carolina, USA4y exp

“AI/LLM engineer focused on building reliable, scalable multi-agent and RAG-based pipelines across microservices. Stands out for combining practical experimentation with strong engineering discipline around schema validation, retries, observability, and structured API contracts to make LLM systems production-ready.”

“Full-stack engineer with a strong builder mindset who has shipped both enterprise workflow software and AI-powered assistant platforms. They combine React/TypeScript and Node.js depth with hands-on experience in LLM/RAG systems, vector search, and reusable MCP-based agent infrastructure, and have delivered customer-facing products for enterprise operations teams including 40+ features across two products.”