Senior Associate/Assistant Vice President, Full Stack Agentic AI Engineer

1 month ago

Singapore, SingaporeSenior

Responsibilities

Design and implement single-agent and multi-agent workflows with orchestration, tool calling, memory, state management, and planning or reasoning loops.
Build production RAG pipelines covering document ingestion, chunking, embeddings, vector stores, semantic search, and hybrid retrieval.
Develop secure tool and API integrations with investment data platforms, portfolio management systems, market data APIs, and internal knowledge bases.
Build prompting, context management, MCP connectors, reusable tool integrations, and evaluation-driven prompt optimization systems.
Develop full-stack AI applications with front-end interfaces, back-end services, RESTful and streaming APIs, enterprise middleware, and WebSocket-based live updates.
Design API contracts, versioning, documentation, data access layers, structured extraction, and integrations that keep AI product knowledge bases current.
Implement observability, monitoring, alerting, safe fallback and human escalation paths, testing, evaluation suites, regression testing, and integration testing.
Participate in code reviews, architecture discussions, production incident response, and continuous reliability improvement.

5-8 years of engineering experience, including at least 2 years focused on AI/ML engineering.
Hands-on experience designing, building, deploying, and operating production-grade AI systems and shipping LLM-powered products to real users.
Experience with agentic AI frameworks such as LangChain, LangGraph, AutoGen, or CrewAI, or equivalent tools.
Strong familiarity with Anthropic and OpenAI SDKs, including tool-calling and streaming APIs.
Proficiency in Python, including FastAPI, asyncio, and Pydantic, plus TypeScript or JavaScript and experience with React, Next.js, and Node.js.
Experience with LLM integration, embedding models, vector databases, RAG components, evaluation frameworks, and AI observability tooling.
Experience with Docker, Kubernetes, AWS, Azure, or GCP, CI/CD pipelines, secrets management, and basic AI security practices.
SQL proficiency and familiarity with data pipeline tooling such as dbt or Airflow.
Financial services, fintech, or data-intensive regulated-industry experience is advantageous.

1,001-5,000 employees