Nimit Kanani

Software Engineer

San Francisco, California, United States6 yrs 11 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Expert in Machine Learning and Data Science applications.
Proven track record in optimizing ETL data pipelines.
Strong background in software engineering and cloud technologies.

Stackforce AI infers this person is a Backend-heavy Fullstack Engineer with expertise in SaaS and Fintech.

Contact

Skills

Core Skills

Machine LearningCloud ComputingData ScienceSoftware Engineering

Other Skills

Go (Programming Language)LangChainKubernetesLarge Language Models (LLM)Python (Programming Language)Deep LearningGenerative AISystem DesignCode ReviewInfluxDBMavenGitApache KafkaStripe (Software)Debugging

About

I am pursuing an MS in Computer Science(AI specialization) at NYU, and I am deeply passionate about Machine learning and its application in Finance, Business, and Healthcare domains. Before my master's, I worked as a backend Software Engineer in the domain of ETL/ELT data pipelines at Hevo Data for 3 years. Looking forward to opportunities at the intersection of my skill set and interests.

Experience

6 yrs 11 mos

Total Experience

1 yr 7 mos

Average Tenure

4 mos

Current Experience

Google

Software Engineer

Jan 2026 – Present · 4 mos · Mountain View, CA · Hybrid

Google Play Monetary Abuse

Visa

Machine Learning Engineer

Jul 2025 – Jan 2026 · 6 mos · Foster City, California, United States · Hybrid

AIaS(AI as a service) Hyperscale Platform team

Go (Programming Language)LangChainKubernetesMachine LearningCloud Computing

New york university

Teaching Assistant

Sep 2024 – May 2025 · 8 mos · Brooklyn, New York, United States · On-site

Teaching Assistant for course "Software Engineering" under Professor Eugene Callahan.

Sov.ai

Quantitative Developer

Jun 2024 – Dec 2024 · 6 mos · Boston, Massachusetts, United States · Remote

Developed a Python software package for extracting, parsing, and analyzing USPTO patent data, automating periodic data processing scripts to generate actionable insights.
Trained a custom sentence transformer language model to map patent assignee organization names to stock market tickers, enhancing data linkage and market analysis.

Large Language Models (LLM)Python (Programming Language)Data ScienceMachine Learning

New york university

Graduate Research Assistant

Oct 2023 – Feb 2024 · 4 mos · Brooklyn, New York, United States · On-site

Applied deep learning models and generative AI to dynamically extract features from e-commerce
webpages, for an NSF-funded research project at the Visualization and Data Analytics lab.
Optimized data collection pipeline, enhancing throughput by 30% and implementing robust handling of
loading failures for improved system resilience.

Deep LearningGenerative AIData SciencePython (Programming Language)Machine Learning

Hevo data

Software Engineer

Jul 2020 – Jun 2023 · 2 yrs 11 mos · Bangalore Urban, Karnataka, India · Hybrid

Streamlined billing/payments microservice to achieve scalability, low latency, and reliability, reducing the rate limiting errors from 1M/month to 0, and the number of billing issues per month by 50%, latency of API calls by 30%.
Developed an on-demand credit feature allowing users to get extra usage for ETL pipelines as credit,
eliminating 95% of cases of pipeline downtime due to usage exhaustion.
Integrated dbt(data build tool) for advanced ELT data warehouse transformations in the product as a core member of a cross-functional team, to facilitate an end-to-end data solution for all customers.
Implemented solutions for recurring issues in ELT data transformations related to failures, timeouts, scalability, and data mismatch, resulting in a 90% reduction in customer-reported technical issues.
Resolved customer issues in ETL/ELT pipelines, and oversaw application infra as part of on-call while collaborating with technical support and DevOps in a fast-paced, high-pressure environment.
Built an admin panel application that provides finer control over the product configurations(pipeline, customer, infra settings) and application monitoring with built-in maker-checker and auditing, reducing the turnaround time of support tickets by 30%, thus increasing customer satisfaction.

System DesignCode ReviewInfluxDBMavenGitApache Kafka+14