At a glance
- Experience
- 1-3 years experience
- Employment
- Full-time
- Workplace
- Onsite
- Location
- Pune
About the role
- AppZen is looking for an AI/ML Engineer with 1–3 years of experience to join the growing AI team in Pune.
- In this role, you will build and ship LLM-powered and agentic features that read, understand, and make decisions on invoices, expense reports, and card transactions.
- You will work alongside senior AI/ML engineers, data scientists, and product managers to take features from prototype to production and own them post-launch.
Responsibilities
- Build and improve LLM-powered features and multi-step agentic workflows that extract, validate and reason over invoices, receipts, expense reports and card transactions.
- Build retrieval pipelines including chunking, embeddings, vector search and re-ranking that give models the right policy, vendor and transaction context to make accurate financial decisions.
- Implement function calling and API integrations so that models can reliably query internal services and ERP/AP systems, and return validated, structured (JSON) outputs.
- Build evaluation datasets, metrics and automated test suites to measure accuracy, hallucination rate, latency and cost, and use the results to improve prompts, retrieval and models.
- Package and deploy models and services using Docker and CI/CD pipelines, add monitoring and logging, and keep them reliable, scalable and cost-efficient in production.
- Work closely with senior engineers, data scientists and product managers to turn customer and business requirements into well-scoped technical solutions, and contribute to code reviews and design discussions.
Requirements
- 1–3 years of experience building and deploying ML/AI solutions in production, including at least 1 year of hands-on work on LLM-based applications.
- Strong Python skills, with experience writing clean, tested, production-grade code and building REST APIs.
- Hands-on experience with LangChain, LangGraph, LlamaIndex or similar frameworks, and with LLM APIs or open-source models.
- Practical experience building RAG pipelines using vector databases such as Pinecone, Milvus, pgvector or FAISS.
- Good understanding of prompting techniques and of getting reliable structured outputs and function calls from LLMs.
- Solid grounding in ML and NLP fundamentals, with working experience in PyTorch or a similar framework.
- Working knowledge of Docker, Git and CI/CD, and experience deploying on a cloud platform.
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
Preferred qualifications
- Exposure to fine-tuning (e.g., LoRA)
- Familiarity with LLM observability tools (e.g., LangSmith, Arize Phoenix or Weights & Biases)
- Experience with document AI (OCR, invoice or receipt data extraction) or with finance, accounts payable or expense data.
Skills
- Python
- FastAPI
- Flask
- LangChain
- LangGraph
- LlamaIndex
- OpenAI
- Anthropic
- Gemini
- Pinecone
- Milvus
- Pgvector
Show all 15 skills
- FAISS
- PyTorch
- Docker
