Applied AI Engineer
Our client, a leading global, multi-strategy hedge fund, is looking for a versatile and driven Applied AI Engineer to join its fast-paced team in London to build AI solutions for internal-facing business functions. You will be automating and optimizing processes and building tools in a low-code environment. You'll work across the full analytics lifecycle, from idea generation to deployment, developing systems that extract value from data and support strategic decisions.
You will be involved in every stage of the analytics process, from research and idea generation to the development and deployment of systems that store, analyze, and extract value from data. Collaborating with cross-functional teams, your work will support strategic decision-making across various business areas and drive innovative solutions. Your projects will initially focus on building a data-driven recruitment pipeline and enabling the Business Development team to attract world-class talent. You'll work with a modern cloud data platform (Snowflake) and data from systems like Salesforce, Workday, and external sources.
Responsibilities
- Lead requirements gathering and solution design with end users.
- Perform data analysis to support strategic decisions.
- Design and implement data models and algorithms, including generative AI.
- Build scalable systems for managing and analyzing large datasets.
- Develop data pipelines from sources like Salesforce to Snowflake.
- Apply NLP and generative AI to improve data quality.
- Research and implement innovative solutions for data-driven recruitment.
Requirements
- 2-5 years in AI/ML engineering with 4-10 years in data roles and a degree in Computer Science, Engineering, Mathematics, or related field.
- Skilled in data visualization (PowerBI, Tableau, Looker, D3.js, R).
- Strong Python programming and statistical analysis skills.
- Experience with tools like Vector Databases, Airflow, AWS, Spark, Databricks, DBT.
- Familiarity with NLP and generative AI techniques.
- Knowledge of cloud platforms (AWS, Azure, Google Cloud).
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