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Vetted Research Assistants in the Greater Seattle

Pre-screened and vetted in the Greater Seattle.

DockerPythonAmazon S3AWSJavaPostgreSQL
SG

Srinivasan Gomadam Ramesh

Senior Software Engineer specializing in LLM agents and RAG pipelines

Redmond, WA7y exp
Quadrant TechnologiesUniversity of Texas at Dallas
AI SystemsLLM AgentsRetrieval-Augmented Generation (RAG)LLM Fine-TuningLLM EvaluationPrompt Optimization+71
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SK

Sriram Krishna

Screened

Mid-Level Software Engineer specializing in AI/ML and cloud-native platforms

Redmond, WA5y exp
Quadrant TechnologiesSeattle University

Backend/AI engineer who has built production LLM orchestration and agentic workflow systems in Python/FastAPI on Kubernetes across AWS/Azure. Demonstrated strong reliability engineering by debugging a real-world memory retention issue that caused latency spikes/timeouts, and strong data/performance chops with a PostgreSQL optimization that cut query latency from ~1.2s to ~15ms. Targets roles building scalable, guardrailed AI-driven workflow automation with robust observability and human-in-the-loop controls.

PythonC#JavaJavaScriptTypeScriptSQL+145
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KP

Karthik Patralapati

Screened

Mid-level AI/ML Software Engineer specializing in GPU-optimized LLM inference and cloud microservices

Seattle, WA5y exp
DVR SoftekSan José State University

Built and deployed a production RAG-based multilingual analytics assistant for healthcare operations, enabling non-technical teams to query claims/EHR and risk metrics with grounded explanations. Demonstrates strong end-to-end LLM system engineering (retrieval tuning, re-ranking, hallucination controls, verification layers) plus workflow orchestration (Airflow/Composer/Step Functions) and stakeholder-driven iteration via prototypes and dashboards.

PythonPandasNumPyPySparkCC+++197
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AT

Akarshika Tripathi

Screened

Mid-level Python Developer specializing in backend microservices and distributed systems

Seattle, WA5y exp
Northern Arizona University

Python backend developer from Larix Technologies who built and scaled microservice APIs for an omnichannel messaging SaaS (WhatsApp/Instagram/Facebook) and led production performance fixes during peak traffic, cutting webhook latency ~50%. Also shipped applied AI products end-to-end: a RAG-based PDF assistant (LangChain + Mixtral via Groq + React) and a BI agent that plans/executes/verifies multi-step analytics with strong guardrails and auditability.

PythonJavaScriptTypeScriptSQLJavaDjango+102
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