Distribution & Analytics GenAI Forward Deployed Engineer

About Our Company We’re a diversified financial services leader with more than $1.5 trillion in assets under management, administration and advisement as of year-end 2024. Our team of 22,000 people across 19 countries, serves more than 3.5 million individual, small business and institutional clients. We are a longstanding leader in financial planning and advice, a global asset manager and an insurer.

Our unwavering focus on our clients and strong financial foundation connects each of our unique businesses – Ameriprise Financial, Columbia Threadneedle Investments and River Source Insurance and Annuities. Here, we foster meaningful careers, invest in the future, and make a difference for clients, institutions and communities around the world.

Job Description Join a high-impact Data & AI engineering team at the forefront of Columbia Threadneedle’s enterprise AI transformation. You’ll build production-grade AI solutions (LLMs, RAG, and agent workflows) used directly by Sales, Marketing, and Distribution teams across EMEA.This is a hands-on engineering role where you will work with modern cloud-native data platforms and AI tooling to turn commercial data into actionable intelligence.

You will contribute to a growing AI agenda with direct exposure to senior leadership and real business impact.

Key Responsibilities Build AI & Advanced Analytics Solutions Design and develop AI workflows using LLMs, RAG, and agent-based frameworks

Implement solutions using AWS Bedrock, Snowflake Cortex, and related services

Build ML feature pipelines and support AI use case deployment

Collaborate with data science teams to product ionise AI capabilities

Develop Cloud Data Pipelines Build and maintain scalable data pipelines across ingestion, transformation, and serving layers

Work with AWS services (S3, Glue, Lambda, Athena) and Snowflake/Redshift Write production-grade code in Python and/or Py Spark Integrate  Commercial & CRM Data Design and implement integrations for Salesforce CRM, Marketing Cloud, and web analytics platforms

Work at API and schema level to connect multiple data sources

Identify and resolve data quality issues across pipelines and source systems

Drive Engineering Quality Contribute to code reviews, testing practices, and pipeline reliability improvements

Participate in technical design discussions and architecture reviews

Identify and address technical debt to improve platform performance

Prototype & Innovate Lead proof-of-concept work for new AI and data capabilities

Evaluate emerging tools and frameworks and recommend adoption

Translate experimentation into practical solutions for the platform roadmap

Tech Stack Cloud: AWS (S3, Glue, Lambda, Athena)
Data Platforms:
Snowflake, Redshift

Languages:

Python, PySparkAI/ML: AWS Bedrock, Snowflake CortexAI Patterns: LLMs, RAG, vector databases, agent frameworksCRM & Data Sources:
Salesforce, Marketing Cloud, third-party commercial data Visualisation (Optional):
Tableau or equivalent

Required Qualifications This role is ideal for a mid–senior engineer who is already operating independently and delivering high-quality solutions but is not yet in a full architectural leadership position.
4–7 years of experience in data engineering, analytics platforms, or software development

Proven track record of hands-on delivery in cloud-native environments Bachelor’s degree in computer science, Engineering, or equivalent experience Strong hands-on experience in cloud data engineering (AWS + Snowflake/Redshift) Proven experience building AI/LLM-based solutions using orchestration frameworks and managed services Solid programming skills in Python and/or PySpark Experience in integrating Salesforce CRM data and commercial data sources Understanding of data pipelines across…