Machine Learning (ML) Engineer Intern - (Paid - India/Remote)

About Companies Library Partners Resources Startup Jobs Log in Apply Peakflo Agentic workflows that automate your back‑office operations Machine Learning (ML) Engineer Intern - (Paid - India/Remote) ₹480K - ₹600K INR • IN / Remote (IN) Job type Internship Role Engineering, Machine learning School year Any Visa US citizenship/visa not required Skills Python, Machine Learning, Natural Language Processing, Machine learning, Prompt Engineering Apply to Peakflo and hundreds of other fast-growing YC startups with a single profile. Apply to role › About the role 🚀 Who we are & What we’re building Peakflo is a rapidly growing Agentic AI company. We are revolutionizing the way global finance teams work with our agentic workflows, and we are actively seeking exceptional talent across diverse disciplines to champion this transformation, uniting multidisciplinary expertise to propel our global strategic vision. Our Growth Story : Peakflo is backed by top-tier global accelerators and investors. We are proud alumni of the prestigious Y-Combinator (W22) and the Google AI Accelerator. Our momentum and impact have been recognized globally by top tech and finance publications: TechCrunch Exclusive: Peakflo’s bid to build business payments for Southeast Asia attracts capital, customers PYMNTS: Peakflo Raises $4.1M in Seed Funding to streamline vendor payments Grit Daily: Latest Feature on Peakflo's Innovations Our Culture : We believe in building a vibrant, high-performance culture that rewards curiosity, ownership, and innovation. Our team spans the globe, and we love coming together to solve hard problems and celebrate our wins. Most importantly, we have begun building an environment that provides the support and mentorship needed to succeed, learn, and grow. ❤️ 💻 What we’re Looking For: We are seeking a highly motivated and detail-oriented Machine Learning (ML) Engineer Intern to join our dynamic team. As a ML Engineer Intern, you will play a crucial part in developing and implementing machine learning solutions to drive business growth and improve our products. 💪 What you’ll do Craft voice‑optimized prompt flows: Design conversational flows that account for natural speech patterns—pauses, interruptions, intonation—with goal‑oriented multi‑turn dialogue optimized for voice-only interactions. Ensure prompts are clear for TTS pronunciation (e.g. spelling out email IDs, phone numbers, dates explicitly) to avoid ambiguity Implement agentic architecture and hierarchical workflows: Build finance AI agents that coordinate sub‑agents—for example, a Research Agent to fetch financial data, a Finance Agent to analyze transactions, and an Editor Agent to craft reports. Organize these into hierarchical-sequential or plan‑and‑execute flows for scalability and modularity   Continuous prompt refinement & iteration: Use LLM feedback loops or "self‑reflection" to score outputs, detect hallucinations, and improve prompts over time. Set up pipelines for A/B testing, prompt versioning, and performance QA tailored to financial use cases  Apply expertise in and potentially fine-tune leading LLMs (e.g., Google's Gemini, OpenAI's GPT series, Anthropic's Claude) to optimize AI Finance Employee performance. Optimize overall LLM system performance to ensure low latency and high efficiency across all financial AI applications. Grounding & retrieval true‑fact enhancement: Integrate RAG (retrieval-augmented generation) with enterprise knowledge bases or financial APIs to avoid misinformation or drift—especially for task‑sensitive use cases like invoicing or AR follow-ups. Maintain tight context control around business domains to limit actions only to finance‑specific interactions   Voice integration & prompt‑tech stack collaboration: Collaborate closely with engineering teams to integrate prompts with speech recognition, intent extraction, LiveKit voice infrastructure, and telephony APIs. Ensure client-side and server-side orchestration maintains real‑time responsiveness and low latency in voice flows Architect and integrate LLM systems with a