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Vaibhav Sharma
Mid-level Software Engineer specializing in AI/ML and data platforms
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About
AI/ML engineer who built a production agentic system to automate computational research experiments (simulation execution, parameter exploration, and numerical analysis) and mitigated context-window failures using constrained tool-calling/prompt-chaining patterns in LangChain with OpenAI tool-enabled models. Also has adtech/big-data pipeline experience at InMobi, orchestrating Spark jobs in Airflow to filter bot-like user IDs and publish clean IDs to an online NoSQL store for live serving, plus Apache open-source collaboration experience.
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Key Strengths
- Built and deployed an end-to-end agentic system to automate computational research experiments (execution, analysis, re-exploration)
- Solved LLM context-window/overflow issues via constrained tool-calling and prompt chaining; appended only tool outputs to history to reduce memory growth
- Designed reliability evaluation for agents using synthetic test cases, failure injection, and measurable metrics (latency, failure rate, tool success, precision/recall)
- Production data pipeline orchestration with Airflow for Spark workflows (user ID augmentation and suspicious/bot ID filtering) feeding an online NoSQL serving store
- Strong stakeholder collaboration: translates research/product goals into requirements docs, prototypes iteratively, then production design/implementation
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