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Opportunity

Join us to tackle one of the most complex problems at the intersection of AI and finance: creating systems that perform deep, multi-step reasoning over complex financial data, legal documents, and market contexts.

This role is about moving beyond pattern matching to build models that truly understand and analyze credit risk.

Responsibilities

Design and implement stateful, multi-agent pipelines that are capable of performing complex credit analysis.

Advanced inference prompt optimization develop and optimize prompt chains and pipelines using frameworks like DSPy and GEPA to programmatically manage and tune reasoning steps, moving beyond brittle, hand-crafted prompts.

Implement and experiment with techniques such as Chain-of-Thought, Tree-of-Thought, and Graph-of-Thoughts to enhance reasoning capabilities.

Create a rigorous evaluation system using LLM-as-a-judge and scenario-based testing to

measure accuracy, robustness, and reasoning quality.

Architect memory management, retrieval, and orchestration strategies to support multi-agent workflows with human-in-the-loop review.

Instrument our AI systems for complete traceability and observability, logging all agent actions, tool calls, and intermediate reasoning steps for debugging, audit, and compliance.

Develop ETL pipelines and data engineering workflows to handle structured, unstructured, vector, and graph data.

Build dashboards to track key metrics: cost, latency, correctness, and concept drift.

Maintain AI services on cloud environments (AWS, Azure) and integrate them into broader DevOps pipelines.

Qualifications

5+ years of commercial development experience in Python or JS.





Demonstrated experience building and deploying production-level Agentic AI or complex

reasoning systems.

Deep expertise in the up-to-date LLM Ops stack: You have hands-on experience with frameworks such as LangChain and evaluation tools (Langfuse, WB, Helicone).

Strong background in data engineering: ETL processes, SQL/NoSQL databases, vector

databases, and graph data models.

Deep understanding of AI agent architectures: prompt engineering, RAG, memory, HITL, tool integration, and multi-agent control (MCP).

Proficiency with cloud platforms (AWS, Azure) and modern DevOps practices (CI/CD,

containerization, infrastructure as code).

Nice-to-have:

Direct experience with RLHF/RLAIF pipelines or model fine-tuning (LoRA, QLoRA).

Experience with graph data models.

Why Join Us

Solve the Hard Problems: You will be working on the frontier of applied reasoning AI, not just another chatbot. Your work will have a direct impact on high-stakes financial decisions.

Build the Foundational Stack: You won t be plugging together APIs. You ll be designing the core evaluation, observability, and reasoning architecture from the ground up.

Unprecedented Impact Ownership: As an early engineer, you will have a significant voice in our technology, architecture, and culture.

Work with a World-Class Team: Collaborate with a logical, product-obsessed founding team and engineers from top-tier backgrounds.

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Applied AI Engineer (Delhi)
🏢 Strategic Talent
📍 Delhi