Senior Forward Deployed Software Engineer - Equities Technology

Millennium•Miami, FL

2d•$175,000 - $250,000

About The Position

The Senior Forward Deployed Software Engineer will work in the Equities Embedded Portfolio Management Solutions Team within Millennium’s Information Technology organization. This hands-on, business-facing role combines solutions architecture, software engineering, and client engagement, partnering with systematic Portfolio Management teams, quantitative researchers, developers, and portfolio managers as a trusted technical advisor. The team accelerates research and trading strategies by delivering expert solutions and architecture across AI, cloud platforms such as AWS and GCP, DevOps, and high-performance computing. Engagements range from advisory and architecture work to embedded implementations of bespoke solutions. The team also builds and maintains managed services, libraries, and reusable patterns that support quantitative research. The engineer translates business and research needs into secure, scalable architectures and production-ready solutions across on-premises, hybrid, and cloud environments. The role spans full-stack development, reusable products and patterns, engagement ownership, technical presentations, and support for managed products and core platforms. It also includes exploring AI, MLOps, cloud and hybrid technologies for advanced use cases, and participating in an on-call rotation.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
At least 6 years of professional software development experience.
Strong computer science foundation, including data structures, algorithms, and system design.
High proficiency in an object-oriented programming language; Python is strongly preferred.
Experience in a customer-facing or business-facing role, with strong emotional intelligence, a customer-first mindset, humility, and commitment to enabling business partners’ success.
Hands-on experience with at least one major cloud provider, including AWS or GCP.
Familiarity with infrastructure-as-code concepts and tools such as Terraform, CloudFormation, or AWS CDK.
Experience designing systems and architectures from ambiguous business needs, with ownership, autonomy, resilience, and composure in demanding, high-pressure environments.
Proficiency with scheduling or asynchronous workflow frameworks and services such as AWS Step Functions, Airflow, Dagster, or Temporal.
Experience with DevOps tools such as AWS CodePipeline, GitHub Actions, or GCP Cloud Build, CI/CD practices, and containerization with Docker and Kubernetes.
Excellent verbal and written communication skills and ability to build relationships and explain complex ideas to diverse audiences.
Pragmatic and adaptable approach that balances technical quality with business needs; willingness to work across architecture, hands-on scripting, and operational support.
Foundational interest in and passion for AI/ML and its practical applications.

Nice To Haves

Experience in financial services or fintech.
Experience building applications on top of LLMs using frameworks such as LangChain or LlamaIndex.
Experience with Retrieval-Augmented Generation patterns.
Experience with MLOps tooling and concepts such as MLflow, model serving, feature stores, or pipeline orchestration with Kubeflow, Vertex AI, or SageMaker.
AWS or GCP cloud certifications at the Associate or Professional level.

Responsibilities

Partner directly with systematic Portfolio Management teams, quantitative researchers, developers, and portfolio managers as a trusted technical advisor.
Understand business and research needs and translate complex or ambiguous requirements into robust, scalable, and secure architectures across on-premises, hybrid, and cloud environments.
Design, build, and deliver production-ready solutions, including Python libraries, Terraform infrastructure as code, CI/CD pipelines, automation scripts, and ML/AI proof-of-concepts.
Develop and maintain managed products, reusable libraries, engineering patterns, and best-practice guides to expand self-service capabilities and accelerate onboarding.
Own embedded engagements from discovery and planning through implementation, knowledge transfer, and support; act as the primary technical point of contact and help remove blockers.
Prepare and deliver presentations, architectural diagrams, and software demonstrations for technical and non-technical audiences, explaining complex concepts in business-centric terms.
Build solutions involving AI, MLOps, cloud and hybrid technologies, distributed GPU training, large-scale data processing, and generative AI integration into research workflows.
Participate in the on-call rotation and provide expert-level support for managed products and core cloud, DevOps, and grid platforms.
Turn support challenges into engineering opportunities through automation and AI agents that reduce manual work and improve operational efficiency.