Senior Software Engineer

Senior Software Engineer – AI Agents & Financial Intelligence

Location: Bengaluru
Experience: 5–8+ years

Employment Type: Full-time

Team: Engineering & AI

About the Role

We are building an AI-powered financial research and analysis platform that uses intelligent agents to transform how financial information is discovered, analyzed, and interpreted.

Our platform combines large language models (LLMs), structured and unstructured financial data, and autonomous agent workflows to help users conduct research, analyze companies, understand financial performance, and generate actionable insights.

We are looking for a Senior Software Engineer who can design and build the core systems powering these capabilities.

You will work at the intersection of software engineering, AI, and financial data, building scalable backend services, agent orchestration frameworks, data pipelines, and intelligent research workflows.

We value engineers with strong computer science fundamentals, architectural judgment, product thinking, and the ability to solve complex problems independently.

Key Responsibilities

1. AI Agents & LLM Engineering

• Design and develop AI agents capable of performing multi-step financial research, analysis, and information retrieval.
• Build agentic workflows involving planning, tool execution, reasoning, retrieval, and synthesis.
• Integrate LLMs with internal systems, external APIs, databases, and financial research tools.
• Develop retrieval-augmented generation (RAG) pipelines for financial documents, filings, transcripts, and research materials.
• Build mechanisms for context management, agent state, memory, structured outputs, and multi-agent coordination where appropriate.
• Implement evaluation, testing, and monitoring frameworks to measure agent accuracy, reliability, latency, and cost.

2. Backend Engineering & System Architecture

• Design and develop scalable backend services, APIs, and distributed systems supporting AI-powered applications.
• Architect asynchronous and event-driven workflows for long-running research and analysis tasks.
• Build robust systems for managing task execution, retries, failures, scheduling, and resource utilization.
• Design efficient data models, caching strategies, and storage architectures.
• Write clean, maintainable, well-tested code and take ownership of services from development through production.

3. Financial Data Engineering

• Build pipelines to ingest, process, normalize, and index structured and unstructured financial information.
• Integrate with financial market data providers, regulatory filings, company disclosures, and other financial information sources.
• Develop systems for extracting information from financial statements, annual reports, earnings transcripts, and other documents.
• Ensure data accuracy, consistency, timeliness, and traceability throughout the platform.
• Build systems that support reliable numerical analysis, financial calculations, and source-backed research outputs.

4. Reliability, Performance & Security

• Optimize system performance, inference latency, infrastructure utilization, and LLM API costs.
• Implement comprehensive logging, monitoring, tracing, and observability across backend services and agent workflows.
• Build safeguards against hallucinations, unreliable tool execution, prompt injection, and incorrect financial calculations.
• Design fault-tolerant systems with appropriate security, access controls, and data protection mechanisms.
• Investigate production incidents and continuously improve system reliability.

5. Technical Leadership & Collaboration

• Own the technical design and delivery of complex engineering initiatives.
• Collaborate with product managers, financial analysts, AI engineers, and other stakeholders to translate research workflows into software capabilities.
• Participate in architectural discussions, design reviews, and technology evaluations.
• Mentor engineers through code reviews, technical guidance, and engineering best practices.
• Continuously explore emerging AI technologies and evaluate their practical application to our products.

Required Qualifications

• 5+ years of professional software development experience, with demonstrated ownership of production systems.
• Strong proficiency in Python, including experience building backend applications and APIs.
• Strong foundation in data structures, algorithms, software architecture, and distributed systems.
• Experience designing backend services using frameworks such as FastAPI, Django, or equivalent technologies.
• Strong knowledge of relational databases such as PostgreSQL, including data modeling and query optimization.
• Experience with cloud platforms such as AWS, GCP, or Azure.
• Familiarity with asynchronous processing, message queues, caching, and distributed task execution.
• Experience integrating third-party APIs and processing large volumes of structured or unstructured data.
• Strong understanding of software testing, debugging, CI/CD, and production observability.
• Ability to independently translate ambiguous product requirements into scalable technical solutions.

Preferred Qualifications

AI & Agentic Systems

• Hands-on experience building applications using LLM APIs such as OpenAI, Anthropic, or open-source models.
• Experience with agent orchestration frameworks such as LangGraph, LangChain, CrewAI, or equivalent custom implementations.
• Familiarity with RAG, embeddings, vector databases, hybrid search, and document retrieval.
• Experience with function calling, structured outputs, tool-use protocols such as MCP, and agent execution frameworks.
• Understanding of LLM evaluation methodologies, prompt engineering, context engineering, and techniques for improving output reliability.
• Experience with agent observability, tracing, and production AI monitoring.

Data & Infrastructure

• Experience with Redis, Kafka, Celery, or similar technologies.
• Familiarity with Docker, Kubernetes, and infrastructure automation.
• Experience building document processing, extraction, or data enrichment pipelines.
• Knowledge of search infrastructure such as Elasticsearch, OpenSearch, or vector search systems.

Financial Domain

• Familiarity with equity research, financial statement analysis, valuation, or investment research workflows.
• Experience integrating financial data APIs or working with market data.
• Understanding of financial statements, company fundamentals, earnings reports, and regulatory disclosures.
• Prior experience building products for fintech, investment research, wealth management, or financial analytics.

Prior fintech experience is advantageous but not mandatory. Strong engineering fundamentals and the ability to learn financial concepts are more important.

What Success Looks Like

Within your first 6–12 months, you will have:

• Designed and delivered production-grade AI agent capabilities supporting real financial research workflows.
• Taken ownership of critical backend services or agent orchestration components.
• Improved the accuracy, reliability, latency, or cost efficiency of agent execution.
• Developed reusable infrastructure that accelerates the delivery of new AI-powered research capabilities.
• Contributed to engineering architecture and improved the overall quality and scalability of the platform.

Why Join Us?

• Work on challenging engineering problems at the intersection of agentic AI, financial intelligence, and distributed systems.
• Build AI-powered products that fundamentally change how financial research is performed.
• Take meaningful ownership of architecture, engineering decisions, and product capabilities.
• Work closely with a multidisciplinary team combining software engineering, AI, and financial expertise.
• Experiment with emerging AI capabilities while solving real-world problems that demand accuracy, reliability, and performance.