About the company
Zamp is a financial technology company with a bold vision of reimagining the future of finance. Finance teams globally face manual, repetitive, and cumbersome tasks. We at Zamp want to empower them with time, calm, money, and joy, tackling harder problems. We foresee four key themes shaping the finance org of the future: Increased Connectivity: Fragmentation across banks, partners, and platforms complicates finance. We envision a seamlessly connected future, abstracting away complexity for real-time data access. AI Automation: Manual tasks like data transformation and reconciliation will be automated by intelligent AI agents, allowing teams to focus on strategic endeavours. Democratized Intelligence: AI-driven insights will be universally accessible, empowering better and faster decision-making, leading to exponential growth for companies. Evolving Financial Infrastructure: Regulatory-compliant advancements like real-time payments and blockchain technology will revolutionize financial exchange, driving efficiency and adaptation. Zamp, founded in 2022 by our experienced leader - Amit Jain, an IIT Delhi and Stanford graduate, and industry veteran with over 20 years of experience, previously held significant roles including Managing Director at Sequoia Capital and Head of Asia Pacific at Uber. Our seed funding round raised ~$22Mn from Sequoia Capital, Dara Khosrowshahi (CEO, Uber), Tony Xu (CEO, Doordash), Marcelo Claure (ex-CEO, Softbank International), and other prominent angel investors. At Zamp, we are excited about what the future holds. We are a team of dreamers, builders, product thinkers building for the new world!
Job Summary
Key Responsibilities:
đź“Ť Lead development of cutting-edge GenAI solutions, exploring advanced AI models such as GPT-4, and proprietary generative models đź“ŤArchitect robust AI systems, integrating with enterprise IT infrastructure using cloud services and data engineering tools like Apache Kafka, Spark, and Airflow đź“ŤOptimize data pipelines for efficiency, scalability, and reliability, leveraging parallel processing, distributed computing, and cloud-based technologies as necessary đź“ŤConduct exploratory data analysis (EDA) and feature engineering to extract relevant insights and patterns from multilingual audio and text data đź“ŤEvaluate and benchmark AI/ML models using appropriate metrics and evaluation methodologies, iterating on model design and hyperparameters to improve performance đź“ŤDocument research findings, experimental results, and software development processes to contribute to project documentation and knowledge sharing within the team đź“ŤStay updated with the latest advancements in AI/ML, signal processing, NLP, and data engineering through self-study, literature review, and participation in conferences and workshops
Qualifications & Skill Set Required:
📍Experience with LLMs (GPT-4, Llama-2 etc.) 📍Experience training and deploying deep learning models in production​​ 📍Good working knowledge with tools such as MLFlow, Airflow, databricks, Python, Langchain, Pytorch, Tensorflow 📍We look for strong ML foundations both in traditional ML methods such as logistic regression, gradient boosted decision trees as well as neural networks 📍Experience with deep learning frameworks such as tensorflow, pytorch, and other ML tools such as scikit learn, XGBoost etc 📍Experience with tensorflow/pytorch and experience in using GPU for speeding up machine learning training in tensorflow/pytorch 📍Familiarity with tools like Ray for efficient and parallel training and inference 📍Familiarity with MLOps, model deployment, CI/CD and monitoring 📍Expertise with Graph & Vector databases 📍Proficiency in cloud architectures, data engineering, and security practices in AI
