Overview

AI Engineer (Agentic AI & Regulatory Reporting)

Responsibilities

• Build AI workflows for exception classification, triage, root‑cause analysis, impact assessment and remediation recommendations.
• Develop agentic workflows involving orchestration, tool/function calling, state management and human‑in‑the‑loop validation.
• Correlate new exceptions with historical issues, known root causes, business rules and transaction/data attributes.
• Build Python and SQL‑based services to query, transform and analyze structured enterprise datasets.
• Integrate AI applications with enterprise APIs, databases, workflow/ticketing platforms and internal data sources.
• Develop reusable tools and services that AI agents can invoke for retrieval, investigation, analysis and workflow execution.
• Apply RAG/context retrieval where regulatory documents, historical knowledge or issue repositories need to be searched.
• Implement confidence scoring, validation, guardrails and traceability for AI‑generated outcomes.
• Build backend services and APIs using Python/FastAPI.
• Implement testing, logging, evaluation, observability, exception handling and production engineering practices.
• Collaborate with onshore AI engineers, architects, business analysts, data engineers and application teams.
• Python
• SQL & Data Analysis
• GenAI / LLM Application Development
• Agentic AI / Workflow Orchestration
• APIs / FastAPI
• Tool / Function Calling
• Structured Data Processing
• Problem Solving / Root‑Cause Analysis
• Git / Software Engineering
• RAG / Retrieval
• LLM Evaluation / Guardrails
• Financial‑services experience is preferred but not mandatory.
• Exposure to Regulatory Reporting,
• Capital Markets, trade lifecycle, post‑trade processing, transaction reporting, reconciliations, exception management, risk or controls will be advantageous.
• Familiarity with regulations such as EMIR, MiFID II or SFTR is a plus.

Qualifications

• Basic React or frontend integration experience
• 5–8 years of professional experience in software engineering, AI/ML engineering, data engineering, GenAI or related roles.
• Demonstrated experience building LLM/GenAI applications beyond proof‑of‑concept chatbots.
• Practical experience implementing agentic workflows, tools or orchestration.
• Strong analytical, debugging and problem‑solving skills.
• Ability to collaborate effectively across onshore/offshore engineering and business teams.
• Bachelor's or Master's degree in Computer Science, Engineering, AI, Data Science or a related discipline preferred.
• Group Health Insurance covering family of 4
• Term Insurance and Accident Insurance
• Paid Holidays & Earned Leaves
• Employee Wellness

Nice to have

• Model Context Protocol (MCP) and reusable agent tool interfaces.
• Knowledge Graph / Graph RAG or data‑lineage concepts.
• Vector databases, hybrid search or re‑ranking.
• AI observability and evaluation frameworks.
• Spec‑Driven Development (SDD): ability to translate business/technical requirements into clear specifications, tasks and acceptance criteria before implementation.