AI Engineer

Key Responsibilities:

AI Agent Development:
Design and build multi-agent AI systems capable of orchestrating data discovery, retrieval, and enrichment across databases, APIs, documents, and trading systems.

Data Orchestration & Integration:
Build data pipelines to consolidate and refresh time-series, trade, risk, and market data from structured and unstructured sources (e.g., SQL, REST APIs, CSV, data marts).

Portfolio Analytics Framework:
Develop analytics modules to compute P&L, VaR, exposure, and performance metrics across multiple asset classes.Automate reporting and dashboard updates through AI-driven workflows.

Autonomous Reasoning & Planning:
Leverage frameworks such as LangChain, CrewAI, AutoGen, or Semantic Kernel for autonomous task orchestration (e.g., identifying stale data, refreshing missing datasets, triggering recalculations).

LLM & Retrieval-Augmented Generation (RAG):
Implement RAG pipelines and vector databases to enable natural-language portfolio queries and generate human-readable insights using LLMs (GPT, Claude, Llama, etc.).

Core Competencies:
AI/Agentic Expertise:Strong hands-on experience with LangChain, CrewAI, AutoGen, or similar multi-agent frameworks.Proven track record in building autonomous AI copilots or orchestration systems.

Data Engineering Proficiency:
Solid understanding of Python, SQL, APIs, and data integration frameworks (e.g., Airflow, dbt, PySpark).Exposure to financial data domains such as positions, trades, and risk sensitivities.

Machine Learning & NLP:
Experience with LLMs (GPT, Claude, Llama, etc.), embeddings, and vector search (Pinecone, FAISS, Chroma).Ability to design RAG, summarization, and contextual reasoning pipelines for structured/unstructured data.

Required Qualifications:

Bachelors or Master’s degree in Computer Science, AI, or a related technical field.

8+ years of experience in AI / ML development and data engineering.

Strong programming skills in Python.

Proficiency in LLM integration, data orchestration, and API development.

Understanding of cloud deployment, MLOps, and AI workflow automation.

Preferred Skills:

Knowledge of financial data systems or quantitative analytics.
Familiarity with Docker, Kubernetes, and CI/CD for AI pipelines.Understanding of vector databases and semantic search.Experience in building AI copilots or multi-agent systems for enterprise use.