Adith Mohanty

Software Engineer Intern · San Jose, CA

Built an AI-driven system that benchmarks how LLM agents perform in production, tailored to the mainframe environment the product runs on.

• Built a distributed evaluation harness in Python running 30 concurrent multi-turn agent trajectories against IBM's ZDBA for DB2 at up to 6,000 questions/hour, cutting manual review from days to two hours.
• Designed a model-based grader on watsonx combining rubric-based scoring and pairwise comparison, with a reason-before-score contract and temperature-0 decoding for deterministic, reproducible scores.
• Extended the grader to assess tool-call efficiency against SME-defined expected calls, revealing only 82% of trajectories used tools efficiently versus a 95% estimate, and fed flagged runs into early-stopping logic.
• Validated against a 12,000-question, 15-agent dataset, establishing ground-truth benchmarks at 90% agent accuracy.

Software Engineer · AI-native commercial underwriting

Worked on an AI-native platform for commercial underwriting — document ingestion, AI-assisted suggestions, and smart extraction of financial data.

• Built an LLM classification pipeline (Llama 3 via Ollama) that auto-sorts and tags uploaded lending documents using scope-aware prompts and a keyword fast-path, reducing GPU compute costs by 50%.
• Built a multi-agent extraction pipeline that parsed 200+ earnings reports and financial documents, populating 100+ structured fields per canonical schema at 95% accuracy.
• Implemented formula logic linking extracted line items to auto-calculated derived metrics like EBITDA, adapting across varying company cost structures without manual reconfiguration.

Software Engineer Intern · San Jose, CA

Built and deployed a release-management dashboard for Cisco ACI's 40-person build and infrastructure team.

• Shipped the dashboard on Kubernetes (Django, React, PostgreSQL), tracking regression runs and test-suite results per release and cutting time spent on manual release monitoring.
• Implemented LDAP authentication with role-based access, letting managers reassign and view test-suite ownership.
• Diagnosed slow legacy queries and added a Redis caching layer, cutting dashboard load times from several seconds to roughly 100ms.

Machine Learning Engineer (Contract) · Berkeley, CA

Built sentiment-analysis models and a streaming pipeline to improve sales forecasting.

• Developed sentiment models using VADER NLP, LDA topic modeling, and TF-IDF, trained on 10k+ consumer records scraped from X, Reddit, and Google via SerpApi.
• Cut forecast error (MAPE) by 20% with a streaming ETL pipeline (Kafka, AWS SQS) feeding live sentiment data into the forecasting model.
• Trained models on Berkeley GPU clusters with SLURM scheduling and deployed inference on CUDA-enabled servers.

Python · Socket Programming · Distributed Systems

A distributed routing protocol built from scratch, mirroring the core mechanisms behind BGP and RIP.

• Routers exchange advertisements and run Bellman-Ford to compute shortest-path forwarding tables across a multi-router network simulation.
• Implemented loop prevention (split horizon, poison reverse) and convergence optimizations (triggered updates, route expiration) for fast, stable reconvergence after topology changes.