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

Apply using this link - https://app.dover.com/apply/Peakflo/f885883d-1ae9-48a7-bd64-8e2898be07b7?rs=42706078

🚀 What we’re building

• Finance operations at any high-growth SMB or startup are plagued with resource-intensive customer collections and vendor payment processes. This culminates into hundreds of wasted finance manhours and thousands of dollars in payment fees!
• Peakflo with its simple API and one-click accounting software integrations, allows businesses to streamline their customer collections and vendor payments. 187 finance team users, from early-stage startups to unicorns in SE Asia, use Peakflo each week to:

✅Save 100 hours/month on finance ops ⏳

✅Get paid faster on customer invoices by 10-20 days 📈

✅Streamline vendor payments and save 50-90% on fees 💰

Most importantly, we have begun building an environment that encourages intellectual curiosity, problem-solving, and ownership. 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 wide range of third-party tools and platforms to facilitate diverse use cases, including email interactions and user chat interfaces.

• AI Solution Development - Develop and optimize complementary AI components such as advanced customizable OCR models, intelligent chatbots, and automated approval systems to support financial workflows.

• Maintain a strong understanding of and stay current with the latest advancements, research, and best practices in large language model (LLM) technologies and AI to drive continuous innovation.

🕵️‍♀️ Who we’re looking for

• Bachelor's or Master's degree in Statistics, Machine Learning, Data Science, or a related field.
• 0.5 - 2 years of industry experience with Machine Learning, Statistics and / or LLM fine-tuning and prompt engineering.
• Excellent written and verbal communication skills in English.
• Extensive experience in Python programming.
• Proficiency with cloud platforms like Google Cloud.
• Strong expertise in Python back-end development and launching ML products in production.
• Passionate about AI and its potential to transform businesses.

➕ We’re Particularly Interested In People Who Have:

1. Experience with multiple LLM platforms and frameworks.
2. Familiarity with natural language processing (NLP) techniques and libraries.
3. Knowledge of software engineering best practices and version control systems (git)

🙂Benefits :

• Competitive stipend
• Performance based full-time role conversion
• Benefits package (post full-time conversion)
• Opportunity for career growth and skill development.
• Collaborative and innovative work environment.
• Flexible work hours and remote work options.

Apply using this link - https://app.dover.com/apply/Peakflo/f885883d-1ae9-48a7-bd64-8e2898be07b7?rs=42706078