Company Description
Amunra Advisors LLP is a quantitative investment firm focused on the Indian markets. Following a multi-strategy, multi-manager approach, the firm drives consistent risk-adjusted returns. Amunra employs rigorous, institutional-grade manager research to identify top talent and strategically allocate capital across established and emerging managers. By leveraging innovative strategies and expertise, Amunra provides a dynamic and results-driven investment framework.
Role Description
Amunra is building OpenBrain, a firm-wide intelligence platform that integrates Generative AI, Second Brain knowledge architecture, and a dynamic Knowledge Graph to enhance investment decision-making.
We are looking for a Senior GenAI / MLOps / Data Engineer to own the complete data-to-intelligence lifecycle. This includes procuring and cleaning high-quality financial data, building robust data pipelines, constructing and maintaining a Knowledge Graph, and productionising advanced Generative AI systems.
This is a high-impact, hands-on role requiring strong technical depth across Data Procurement, Data Cleaning, Data Engineering, Knowledge Graph Engineering, Generative AI, and MLOps.
Key Responsibilities
Data Procurement & Ingestion
- Identify, evaluate, and integrate diverse financial and alternative data sources, including APIs, Bloomberg, regulatory filings, earnings transcripts, research reports, web sources, and other structured or unstructured datasets.
- Build automated, reliable, and compliant data procurement pipelines that can support research, investment workflows, and AI-native applications.
Data Cleaning & Ingestion
- Design and implement large-scale data cleaning, deduplication, normalisation, validation, and enrichment processes.
- Work extensively with unstructured and semi-structured financial documents such as PDFs, filings, transcripts, reports, and research notes.
- Use both traditional data processing techniques and LLM-powered methods to ensure data is accurate, well-structured, and AI-ready.
Data Engineering
- Build scalable batch and real-time ETL/ELT pipelines using modern data engineering tools and frameworks such as Spark, Kafka, Airflow, Dagster, dbt, or equivalent technologies.
- Design and maintain data lakes, lakehouses, and storage architectures optimised for Generative AI, Knowledge Graph, and retrieval workloads.
Knowledge Graph & Second Brain Architecture
- Architect, build, and evolve a large-scale Knowledge Graph for financial and investment intelligence.
- Develop LLM-augmented systems for entity extraction, relationship mining, ontology management, temporal reasoning, and knowledge enrichment.
- Implement advanced retrieval systems, including vector search, graph retrieval, hybrid retrieval, and agentic RAG.
Generative AI & Agent Development
- Build, fine-tune, and productionise Generative AI systems, including multi-agent workflows, financial copilots, reasoning engines, and domain-specific LLM applications.
- Develop robust evaluation frameworks to assess accuracy, reliability, hallucination risk, retrieval quality, and system performance.
MLOps & Production AI Systems
- Own the end-to-end ML and AI lifecycle, including experimentation, training, deployment, monitoring, drift detection, observability, and governance.
- Design scalable, cost-efficient inference platforms using Kubernetes and tools such as vLLM, Ray, MLflow, LangSmith, Docker, LangChain, and LangGraph.
Requirements
Must Have
- 3-6 years of experience across Data Engineering, Generative AI, MLOps, or related infrastructure-heavy AI/ML roles.
- Hands-on expertise in data procurement, data cleaning, and processing unstructured financial documents such as PDFs, transcripts, regulatory filings, and research reports.
- Experience building Knowledge Graphs and advanced RAG systems using technologies such as Neo4j or equivalent graph databases, along with vector databases.
- Strong proficiency in LLM workflows, including prompt engineering, fine-tuning, evaluation, retrieval augmentation, and agentic systems.
- Production-grade experience with data pipelines and MLOps tools such as Python, Spark, Kafka, Airflow, Kubernetes, Docker, MLflow, LangChain, LangGraph, or similar platforms.
- Strong software engineering fundamentals, clean coding practices, system design ability, and a production-first mindset.
- Ability to operate in a hands-on, high-ownership environment where reliability, scalability, and quality matter.
Strong Plus
- Experience working in quantitative hedge funds, asset management, capital markets, financial research, or trading environments.
- Experience with multimodal document intelligence models and tools such as LayoutLM, Donut, LLaVA, or similar systems.
- Open-source contributions, technical publications, or demonstrated thought leadership in RAG, Knowledge Graphs, AI infrastructure, or Data Engineering for AI.
Ideal Candidate
- The ideal candidate is a senior hands-on builder who can move fluently across data infrastructure, AI engineering, and production systems.
- You are comfortable working with messy financial data, designing reliable pipelines, building intelligent retrieval systems, and deploying GenAI applications that must be accurate, observable, and useful in real-world investment workflows.
- You combine strong engineering discipline with curiosity about markets, research, and how institutional knowledge can be transformed into durable investment intelligence.
