AI/ML Engineer

Job Description:

The Role

In this role, candidate will be part of the squad that will be working on growing FI Investment sales and platform business. Candidate will work on generating opportunity leads, product recommendation, setting strategy of coverage, engagement optimization, business optimization and experimentation. The goal of the squad & the candidate is to research, develop and implement AI and ML methodologies to achieve business objective.

• Implementing AI and ML techniques to further and Grow the FI business

• Working closely with business partners to understand business problems and providing data solutions

• Researching, developing, and implementing new analysis and ML techniques

The Expertise and Skills You Bring

• Education: BS/MS in Engineering, Computer Science, Data Science, or a related field.

• Problem Solving: Strong problem-solving skills and mathematical thinking.

• Analytic Skills: Strong analytic skills related to working with both structured and unstructured datasets.

• Experience: 1-3 years of software development experience with a desire to work on a fast-paced development team and handle multiple tracks concurrently.

• Programming: Proven experience developing with object-oriented/object function scripting languages like Python.

• Data Analysis: Experience performing explorative data analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.

• ETL Tools: Experience with developing ETL data pipeline tools such as dbt, Luigi and Airflow preferred.

• Distributed Computing: Understanding of distributed computing principles and working experience with big data tools such as Spark, Hadoop, Kafka, Hive, Impala, and Snowflake preferred.

• Tool Stack: Knowledge of Pip, Flask, NumPy, Anaconda, or similar tool stack.

• Cloud Computing: Knowledge of cloud computing concepts (AWS) and working experience with deploying and managing applications in the cloud.

• Statistical Models: Knowledge of statistical models, predictive models, and time series analysis (such as regression, classification, simulation, dimension reduction).

• Large Language Models (LLM): Familiarity with large language models (LLMs) and their applications in natural language processing (NLP) tasks.

The Team

Our team supports a variety of complex applications serving a diverse set of businesses and customers directly. The database environments are extremely critical to our success and vitality which in turn requires a unique technical aptitude.

Certifications:

Category:

Fidelity’s hybrid working model blends the best of both onsite and offsite work experiences. Working onsite is important for our business strategy and our culture. We also value the benefits that working offsite offers associates. Most hybrid roles require associates to work onsite every other week (all business days, M-F) in a Fidelity office.