Senior Artificial Intelligence Engineer

Key Responsibilities

1. Team Leadership and Technical Direction

• Lead, mentor, and manage a team of AI Engineerssetting technical direction, reviewing architecture and code, and supporting their day-to-day growth.
• Act as the primary point of technical escalation for the AI engineering function, liaising with the MD and cross-functional stakeholders on priorities, timelines, and risk.

2. Agentic AI Platform Architecture

• Drive the architecture of the agentic AI platform end to end, spanning agents, MCP servers, and supporting services that propose and apply changes across large codebases and financial workflows.
• Define best practices around concurrency, telemetry, configuration hygiene, prompt versioning, and performance-sensitive code paths, so AI outputs remain reliable and idiomatic.

3. Retrieval Systems and AI Engineering

• Design and oversee retrieval systems (RAG, vector search, hybrid approaches) that give AI agents and developers accurate, up-to-date context from large codebases, financial documents, and design artifacts.

4. Evaluation and Quality Assurance

• Build and refine compile, test, and evaluation pipelines, covering static analysis, style and safety checks, performance gates, and code review, to consistently measure and raise the quality of AI-generated changes.
• Drive experiments and evaluation frameworks to continuously improve AI-driven workflows across the team.

5. Compliance and Information Security

• Comply with IT policies and procedures.
• Maintain security of information at all times.

Requirements

1. Engineering Experience

• 3+ years of overall engineering experience, including demonstrable experience building and shipping AI/LLM-powered systems.
• Strong proficiency in Python (Java a plus), with hands-on experience in production-grade software systems.

2. Leadership Experience

• Prior experience leading, mentoring, or managing engineers, or clear readiness to step into a team-lead role.

3. Agentic AI and Multi-Agent Systems

• Proven experience with agentic AI frameworks and patterns (LangChain, LangGraph, AutoGen, CrewAI, or similar) and multi-agent orchestration.
• Experience with MCP (Model Context Protocol) servers or comparable tool/agent-integration architectures.

4. Retrieval Systems

• Practical experience with retrieval systems—vector search, embeddings, RAG pipelines, or hybrid retrieval approaches.

5. Communication and Stakeholder Management

• Strong communication skills, with the ability to translate technical decisions for non-technical stakeholders and senior leadership.

6. Work Environment

• Comfort operating in a fast-paced, high-trust environment handling sensitive financial data.

Strongly Preferred Skills

• Experience in fintech, financial services, or investment/deal-related domains (M&A, private equity, IPO due diligence, or similar).
• Familiarity with compiler/static analysis tools or large-scale refactoring systems.
• Experience fine-tuning or customizing open-weight models.
• Knowledge of model-serving infrastructure and evaluation/observability tooling for LLM systems.
• Exposure to security-conscious deployment practices (OWASP LLM Top 10, API hardening, audit logging).

Educational Qualification

• Bachelor’s or Master’s in Computer Science (or related) with strong fundamentals (algorithms, data structures, systems).