Job Description:
Seeking a hands-on AI Engineer/Developer to rapidly prototype and deliver AI-driven solutions across investment, research, and trading functions. This role is ideal for someone who can bridge financial domain knowledge with applied AI/ML development, building proof-of-concepts that can evolve into production-grade tools supporting investment decision-making.
Key Responsibilities
- Design and develop AI/ML prototypes using Python to support investment research, portfolio analytics, and trading insights
- Build and test LLM-based and data-driven use cases (e.g., research summarization, signal generation, document analysis, automation)
- Work closely with investment professionals, research teams, and data engineers to translate business needs into functional AI solutions
- Develop and integrate data pipelines, APIs, and analytical models to support rapid experimentation
- Leverage tools such as Jupyter, Pandas, NumPy, LangChain, and cloud AI services (AWS/Azure/OpenAI)
- Perform data exploration, feature engineering, and model validation on financial datasets
- Iterate quickly on prototypes, incorporating feedback to refine models and outputs
- Ensure solutions consider data security, governance, and compliance within a financial services environment
Required Qualifications
- 10+ years of experience in Python development with strong hands-on coding ability
- Experience building AI/ML or LLM-based applications and prototypes
- Exposure to financial services, asset management, trading, or investment research
- Strong experience with data analysis libraries (Pandas, NumPy) and working with structured/unstructured data
- Familiarity with APIs, microservices, and cloud platforms (AWS, Azure, or GCP)
- Ability to work in a fast-paced, iterative environment with direct interaction with business stakeholders
Preferred Qualifications
- Experience with LLM frameworks (LangChain, RAG architectures, vector databases)
- Knowledge of financial data sets, market data, or portfolio analytics
- Experience prototyping tools for quant research, credit analysis, or trading workflows
- Familiarity with Snowflake, Databricks, or modern data platforms
What Success Looks Like
- Rapid delivery of high-impact AI prototypes that demonstrate clear business value
- Strong collaboration with front-office teams to solve real investment and research problems
- Ability to evolve prototypes into scalable solutions in partnership with engineering teams
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