Data & AI Engineer (LLMs & Distribution Analytics)

Job Description

Job DescriptionJob Role: Data & AI Engineer (LLMs & Distribution Analytics)
Job Location: Boston, MA (Day 1 Onsite)
Job Type: Full Time
Salary Range: $100000 to $130000/Annum Full Time Benefits

Job Description
Join a high-impact Data & AI engineering team at the forefront of 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.

Roles & Responsibilities
" Work on enterprise-scale AI initiatives with visible business impact
" Build real-world generative AI solutions (not just prototypes)
" Access cutting-edge technologies including AWS Bedrock and Snowflake Cortex
" Partner with global teams across engineering, data science, and business domains
" Opportunity to shape and scale AI capabilities in a leading asset management firm
" Clear progression into senior AI/data engineering roles
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=9 What Success Looks Like (First 12 Months)
" Deliver production-ready AI use cases (e.g., AI agents for sales and distribution insights)
" Integrate key CRM and marketing datasets into a unified analytics platform
" Improve reliability and scalability of commercial data pipelines
" Contribute to enterprise AI adEligible to workion through working solutions and PoCs
" Establish strong engineering practices in data and AI delivery
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=9 What You ll Do
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 productions 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 PySpark
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 adEligible to workion
" Translate experimentation into practical solutions for the platform roadmap
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=9 Tech Stack
" Cloud: AWS (S3, Glue, Lambda, Athena)
" Data Platforms: Snowflake, Redshift
" Languages: Python, PySpark
" AI/ML: AWS Bedrock, Snowflake Cortex
" AI Patterns: LLMs, RAG, vector databases, agent frameworks
" CRM & Data Sources: Salesforce, Marketing Cloud, third-party commercial data
" Visualization (Eligible to workional): Tableau or equivalent
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=9 Essential Skills
" 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 ingestion, transformation, and serving
" Ability to work Consultantly with strong delivery discipline and ownership.
=9 Nice to Have
" Experience with MLOps, feature engineering, or model serving
" Exposure to multi-agent frameworks (e.g., LangGraph, CrewAI, AWS Agent Core)
" Experience of onboarding third-party commercial datasets (e.g., fund flows, market intelligence)
" Experience with BI tools (Tableau or similar) for business-facing analytics
=9 Experience & Education
" 4 years of experience in data engineering, analytics platforms, or software development
" Proven track record of hands-on delivery in cloud-native environments