Python Developer

Job DescriptionSeeking 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 ResponsibilitiesDesign and develop AI/ML prototypes using Python to support investment research, portfolio analytics, and trading insightsBuild 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 solutionsDevelop and integrate data pipelines, APIs, and analytical models to support rapid experimentationLeverage tools such as Jupyter, Pandas, NumPy, LangChain, and cloud AI services (AWS/Azure/OpenAI)Perform data exploration, feature engineering, and model validation on financial datasetsIterate quickly on prototypes, incorporating feedback to refine models and outputsEnsure solutions consider data security, governance, and compliance within a financial services environmentRequired Qualifications10+ years of experience in Python development with strong hands-on coding abilityExperience building AI/ML or LLM-based applications and prototypesExposure to financial services, asset management, trading, or investment researchStrong experience with data analysis libraries (Pandas, NumPy) and working with structured/unstructured dataFamiliarity with APIs, microservices, and cloud platforms (AWS, Azure, or GCP)Ability to work in a fast-paced, iterative environment with direct interaction with business stakeholdersPreferred QualificationsExperience with LLM frameworks (LangChain, RAG architectures, vector databases)Knowledge of financial data sets, market data, or portfolio analyticsExperience prototyping tools for quant research, credit analysis, or trading workflowsFamiliarity with Snowflake, Databricks, or modern data platformsWhat Success Looks LikeRapid delivery of high-impact AI prototypes that demonstrate clear business valueStrong collaboration with front-office teams to solve real investment and research problemsAbility to evolve prototypes into scalable solutions in partnership with engineering teams