Top Senior Python Jobs in Miami, FL

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing data-processing systems, optimizing quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. The role requires strong backend software engineering skills, production systems experience, and independent problem-solving in an ambiguous environment.

Build and optimize petabyte-scale data pipelines for AI model training, including audio data acquisition, large-scale processing, and dataset quality improvements. Collaborate with AI scientists, operate production systems, and independently manage engineering projects from discovery through continuous improvement. The role emphasizes backend software engineering, scalability, throughput, cost efficiency, and reliable infrastructure.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable data-processing systems, optimizing quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement.

Build and operate large-scale data pipelines for AI model training, including discovering audio data sources, optimizing data processing for quality, throughput, and cost, and collaborating with AI scientists. Own engineering projects from discovery through implementation and continuous improvement. The role requires strong backend software engineering skills, production systems experience, and independent problem-solving in ambiguous environments.

Build and operate large-scale AI training data pipelines, discover audio data sources, optimize data processing for quality, throughput, and cost, and collaborate with AI scientists on next-generation models. The role requires strong backend software engineering, production systems experience, and independent ownership of engineering projects in ambiguous environments.

Build and operate large-scale data pipelines for AI model training, including discovering audio data sources, optimizing processing systems for quality, throughput, and cost, and collaborating with AI scientists. The role requires ownership of engineering projects from discovery through continuous improvement, with a focus on production systems, scalable data processing, and efficient dataset development.

Build scalable AI training-data pipelines and large-scale data-processing systems. Discover and acquire audio data, optimize data quality, throughput, and cost, and collaborate with AI scientists on next-generation models. Own engineering projects from discovery through continuous improvement in a distributed environment.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering audio data sources, developing reliable ingestion and processing systems, optimizing data quality, throughput, and cost, and collaborating with AI scientists on training datasets. The role requires ownership of engineering projects, strong backend development, production systems experience, and independent problem-solving in an ambiguous environment.

Build and operate large-scale data pipelines that collect, process, and optimize audio datasets for AI model training. The role involves discovering data sources, improving data quality, throughput, and cost efficiency, partnering with AI scientists, and owning engineering projects end to end. Strong backend software engineering, Python, Bash, Linux, production systems, and data-processing experience are required, with GCP, Terraform, web crawling, and audio or ML dataset familiarity preferred.

Build and operate large-scale AI data pipelines, discover audio data sources, optimize data-processing systems for quality, throughput, and cost, and collaborate with AI scientists on training datasets. The role requires backend software engineering, production systems experience, independent problem-solving, and ownership of projects from discovery through continuous improvement. Nice-to-have experience includes web crawling, distributed processing, cloud infrastructure, Terraform, dataset quality, and audio processing.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable ingestion and processing systems, optimizing data quality, throughput, and cost, and collaborating with AI scientists. The role requires ownership of engineering projects from discovery through continuous improvement in a distributed, ambiguous environment.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering audio data sources, developing reliable data-processing systems, optimizing quality, throughput, and cost, and collaborating with AI scientists. The role requires ownership of engineering projects from discovery through continuous improvement, with emphasis on backend development, production systems, data acquisition, and scalable infrastructure.

Build and operate large-scale data pipelines that acquire, process, and optimize audio datasets for AI model training. Responsibilities include discovering data sources, improving data quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. The role requires strong backend engineering, production systems experience, and independent problem-solving in a distributed environment.

Build and operate large-scale data pipelines for AI model training, including discovering audio data sources, optimizing processing systems for quality, throughput, and cost, and collaborating with AI scientists. Own engineering projects from discovery through continuous improvement while working independently in an ambiguous environment.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable processing systems, optimizing quality, throughput, and cost, and collaborating with AI scientists. The role requires strong backend engineering, production systems experience, Python and Bash proficiency, and independent ownership of projects. Experience with web crawling, distributed processing, GCP, Terraform, machine-learning datasets, or audio processing is advantageous.

Build scalable data pipelines and processing systems for AI model training at petabyte scale. Responsibilities include discovering audio data sources, improving dataset quality and processing efficiency, optimizing throughput and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement.

Build and operate large-scale audio data pipelines for AI model training. The role involves discovering data sources, optimizing data processing for quality, throughput, and cost, partnering with AI scientists, and owning engineering projects from discovery through continuous improvement.

Owns end-to-end post-sales technical support for federal and special-program customers in secure, classified, and air-gapped environments. Diagnoses issues across Linux, compute, GPUs, networking, storage, cloud, and applications; collects and sanitizes diagnostics; manages escalations; maintains hardened deployments; supports hardware replacement; writes knowledge-base content; and provides customer and pre-sales support. The role includes on-call rotations, secure-facility work, and regional and domestic travel.

Build and evolve scalable full-stack systems for People Data Labs’ APIs, self-serve products, and customer success tools. Responsibilities include designing frontend and backend architecture, developing React and Python applications, ensuring data integrity, implementing reliable APIs, optimizing performance, operating production services, collaborating with stakeholders, writing technical designs, mentoring engineers, and improving development practices.

Lead the technical direction and architecture of the Foundations platform team, owning authentication, authorization, SSO, secrets management, tenancy, and core platform services. Design APIs, drive services through launch, make technical decisions, establish engineering standards, and mentor engineers without formal management authority. Partner with engineering and product leaders to align execution with priorities.

Leads and grows the Payments engineering team while remaining hands-on in software development. Designs scalable backend systems using .NET, databases, messaging, Docker, and AWS. Manages engineering and QA professionals, mentors team members, prioritizes initiatives, and collaborates with product and business stakeholders. The role combines approximately 60% engineering with 40% people and project management.

Build and operate the platform supporting Honeydew’s digital dermatology product. Responsibilities include migrating data infrastructure to RDS, strengthening AWS security and compliance, standardizing logging, developing AI-assisted vulnerability remediation workflows, creating infrastructure and automation, supporting feature teams, and reviewing code. The role requires backend service and API development, platform security, infrastructure as code, CI/CD, and strong data and architecture skills.

Design, develop, deploy, and maintain scalable backend services, APIs, and frontend applications. Own projects from architecture through monitoring, collaborate with product and design teams, participate in technical and code reviews, troubleshoot complex issues, and improve engineering practices. The role focuses on secure, reliable systems across cybersecurity, AI, and distributed systems while providing technical guidance to peers.

Leads a distributed engineering team while contributing hands-on to Azure software development, observability services, cloud architecture, and production reliability. Responsibilities include technical direction, coding, design reviews, customer engagements, incident response, on-call operations, reusable engineering assets, cross-team initiatives, and people development. The role translates customer needs into Azure platform improvements and balances delivery, engineering strategy, service resilience, and talent development.

Designs and manages cloud infrastructure across development, staging, and production using Terraform and AWS. Builds containerized environments with Docker, ECS, and Kubernetes; develops CI/CD pipelines; manages networking, routing, APIs, WAF, monitoring, and autoscaling. Troubleshoots infrastructure and deployment issues, optimizes performance and reliability, and collaborates with development and QA teams. The role also supports cloud-agnostic architecture, observability, Linux administration, and automation scripting.