Principal Solutions Architect, Aws Financial Services, Industry Specialists for Capital Markets

Amazon Amazon · Big Tech · NY +1 · Solutions Architect

Principal Solutions Architect for AWS Financial Services, specializing in Capital Markets. This role focuses on designing and architecting AWS solutions for clients in hedge funds, asset management, and quantitative trading firms, with a strong emphasis on data and analytics, generative AI, and high-performance computing. Responsibilities include migrating data-intensive workloads, architecting generative AI/ML solutions (LLM fine-tuning, RAG, sentiment analysis, agentic AI), and designing HPC environments. The role involves deep technical expertise and customer engagement to accelerate modernization on AWS.

What you'd actually do

  1. Design and architect AWS solutions with a specific focus on data and analytics, generative AI, and high performance compute for capital markets customers, collaborating with AWS Business Development, Partner, and account teams to help hedge funds, asset managers, quantitative trading firms, and broker-dealers migrate to AWS.
  2. Architect solutions for large-scale data ingestion, transformation, and analytics workloads, including real-time and batch processing of market data, fundamental data, alternative data (satellite imagery, NLP on earnings calls, credit card transactions, web scraping), and ESG datasets, leveraging services such as Amazon S3, AWS Glue, Amazon EMR, Amazon Redshift, Amazon Athena, and AWS Lake Formation.
  3. Design and implement generative AI and machine learning solutions for quantamental research, including large language model (LLM) fine-tuning for financial document analysis, retrieval-augmented generation (RAG) architectures for research automation, sentiment analysis on news and social media, and agentic AI workflows for autonomous research and trading signal generation, leveraging Amazon Bedrock, Amazon SageMaker, and AWS Trainium/Inferentia.
  4. Architect high performance compute (HPC) environments for computationally intensive workloads such as Monte Carlo simulations, options pricing, portfolio optimization, and quantitative backtesting, leveraging Amazon EC2 (compute-optimized and memory-optimized instances), AWS ParallelCluster, AWS Batch, and Amazon FSx for Lustre.
  5. Engage directly with senior technical and business leaders at hedge funds (multi-strategy, long/short equity, quantitative, systematic macro), asset managers (active and passive), quantitative trading firms, and broker-dealer research teams to understand their data, analytics, and AI/ML requirements and develop compelling AWS-based solutions.

Skills

Required

  • AWS
  • Data and analytics
  • Generative AI
  • High performance compute (HPC)
  • Capital markets
  • Quantitative research infrastructure
  • Data lake and lakehouse architectures
  • Alternative data integration
  • Backtesting and simulation frameworks
  • Portfolio optimization
  • Risk analytics
  • Large-scale data ingestion, transformation, and analytics
  • Real-time and batch processing
  • LLM fine-tuning
  • Retrieval-augmented generation (RAG)
  • Sentiment analysis
  • Agentic AI workflows
  • Amazon Bedrock
  • Amazon SageMaker
  • AWS Trainium/Inferentia
  • Amazon EC2
  • AWS ParallelCluster
  • AWS Batch
  • Amazon FSx for Lustre
  • Proof-of-concepts
  • Prototypes
  • Reference architectures
  • Data governance
  • Compliance requirements

Nice to have

  • quantitative hedge funds
  • systematic asset managers
  • multi-strategy funds
  • broker-dealer research teams
  • quantamental research shops
  • Jupyter
  • RStudio
  • MATLAB
  • Zipline
  • Backtrader
  • QuantConnect
  • AWS blogs
  • whitepapers
  • Battle of the Quants
  • QuantMinds
  • AWS re:Invent

What the JD emphasized

  • deep expertise in data and analytics, generative AI, and high performance compute
  • architect scalable, performant, and cost-effective solutions
  • hands-on implementation of data pipelines, machine learning models, and HPC clusters on AWS
  • data governance, lineage, and compliance requirements

Other signals

  • architecting generative AI and machine learning solutions for quantamental research
  • design and architect AWS solutions with a specific focus on data and analytics, generative AI, and high performance compute for capital markets customers
  • architect solutions for large-scale data ingestion, transformation, and analytics workloads