About the Role

We are looking for an AI Forward Deployed Engineer (AI FDE) to work directly with business, investment, operations, risk, and technology teams to identify high-value opportunities for applying AI across C apital Markets, Wealth Management, and Asset Management .

This is a high-impact, hands-on role at the intersection of AI engineering, financial services, product development, and client/business problem solving . You will take ambiguous business problems, understand the underlying workflows and data, prototype AI-powered solutions rapidly, and take successful solutions into production.

The ideal candidate combines strong software engineering fundamentals with practical experience building LLM/GenAI applications, agentic workflows, data pipelines, and enterprise AI solutions . You should be comfortable working with senior stakeholders as well as writing production-quality code.

What You Will Do

1. Identify & Solve Business Problems

- Work directly with portfolio managers, investment professionals, wealth managers, research teams, traders, operations, risk, compliance, and senior business stakeholders.
- Understand complex financial workflows and translate business problems into AI-enabled solutions.
- Identify processes where AI can deliver measurable improvements in productivity, revenue, risk management, client experience, or operational efficiency.
- Rapidly prototype solutions and validate them with end users.

2. Build AI-Powered Applications

- Design and build production-grade applications using LLMs, RAG, AI agents, workflow automation, and traditional ML .
- Develop AI copilots and agents for use cases such as:
- Investment research and market intelligence
- Equity, credit and macro research
- Portfolio analysis and monitoring




- Wealth advisor and relationship-manager copilots
- Client reporting and communication
- Investment proposal generation
- Financial-document intelligence
- Earnings and company analysis
- Regulatory and compliance workflows
- Trade and post-trade operations
- Risk monitoring and exception management
- Knowledge management and enterprise search
- Integrate AI applications with internal data, APIs, databases, market-data platforms, and enterprise systems.

3. Own Solutions End-to-End

- Take solutions from problem discovery → prototype → pilot → production → adoption .
- Build scalable APIs, services, data pipelines, and user interfaces where required.
- Establish appropriate evaluation frameworks for LLM and agentic applications.
- Monitor production systems for accuracy, latency, reliability, cost, and business impact.
- Iterate rapidly based on user feedback.

4. Work With Modern AI Technology

- Work with LLMs and foundation models from leading providers and/or internally hosted models.
- Build applications using techniques such as:
- Retrieval-Augmented Generation (RAG)
- Tool/function calling
- Agentic workflows
- Structured outputs
- Prompt engineering
- Fine-tuning where appropriate
- Embeddings and vector search
- LLM evaluation and observability
- Model routing and cost optimization




- Evaluate new AI technologies and determine where they can create practical business value.

5. Financial Services Data & Domain

- Work with structured and unstructured financial data including research reports, filings, presentations, market data, portfolio data, client information, transactions, and internal knowledge.
- Develop solutions that account for the requirements of financial-services environments, including security, privacy, auditability, model risk, data governance, and regulatory controls .
- Understand the difference between an impressive AI demo and a solution that can safely operate in a regulated financial institution.

Required Skills & Experience

- 3–8 years of experience in software engineering, data engineering, ML/AI engineering, quantitative technology, or a similar technical role.
- Strong proficiency in Python and modern software engineering practices.
- Experience building and deploying production applications using APIs, databases, cloud infrastructure, and distributed systems.
- Hands-on experience with Generative AI / LLM application development .
- Strong understanding of RAG, embeddings, vector databases, LLM APIs, tool calling, and agentic architectures.
- Experience with at least one major cloud platform such as AWS, Azure, or GCP .
- Familiarity with contemporary AI/ML frameworks and developer tooling.
- Strong understanding of software development lifecycle, testing, Git, CI/CD, observability, and production engineering.
- Ability to work with SQL and structured/unstructured data.
- Strong problem-solving skills and ability to work with ambiguous requirements.
- Excellent communication skills, with the ability to explain technical concepts to investment and business stakeholders.

📌 AI Forward Deployed Engineer (AI FDE) (Mumbai)
🏢 360 ONE
📍 Mumbai