Overview
Hybrid
Depends on Experience
Contract - W2
Contract - 06 Month(s)
Skills
Python
PyTorch
Amazon Web Services
Artificial Intelligence
Kubernetes
Job Details
Lead Python Application Developer - Local to Tampa, FL Only
Looking for someone local to Tampa Bay Area to work in a hybrid onsite model
Financial Services domain background is highly preferred with at least 10 years of experience
Skills Technical Responsibilities:
Architect, design, and optimize Python-based RAG (Retrieval-Augmented Generation) systems for real-world deployment.
Build and maintain vector databases and embedding pipelines using Redis, Faiss, or similar high-performance stores.
Develop and manage GPU-accelerated pipelines for embedding generation and model vectorization.
Design and operate Kubernetes (K8s) environments for scaling distributed microservices and model workloads.
Integrate retrieval and generation components into larger AI/LLM ecosystems with high throughput and reliability.
Own performance tuning, profiling, and system observability for millisecond-level responsiveness.
Contribute to automation, CI/CD pipelines, and infrastructure-as-code practices.
Collaborate closely with AI engineers, data scientists, and DevOps to deliver production-grade, fault-tolerant systems.
Core Technical Qualifications:
10+ years of experience in Python software engineering for production systems.
Deep expertise with Redis, Kubernetes, and GPU-based computation (CUDA, PyTorch, or TensorFlow).
Proven experience designing and deploying vector databases, embeddings, and RAG architectures.
Strong knowledge of FastAPI, asyncio, and microservice architectures.
Skilled with Docker, Helm, and cloud-native stacks (AWS/Google Cloud Platform/Azure).
Excellent debugging, performance tuning, and system-level problem-solving skills.
Demonstrated ability to deliver high-quality code under pressure and within tight deadlines.
Build and maintain vector databases and embedding pipelines using Redis, Faiss, or similar high-performance stores.
Develop and manage GPU-accelerated pipelines for embedding generation and model vectorization.
Design and operate Kubernetes (K8s) environments for scaling distributed microservices and model workloads.
Integrate retrieval and generation components into larger AI/LLM ecosystems with high throughput and reliability.
Own performance tuning, profiling, and system observability for millisecond-level responsiveness.
Contribute to automation, CI/CD pipelines, and infrastructure-as-code practices.
Collaborate closely with AI engineers, data scientists, and DevOps to deliver production-grade, fault-tolerant systems.
Core Technical Qualifications:
10+ years of experience in Python software engineering for production systems.
Deep expertise with Redis, Kubernetes, and GPU-based computation (CUDA, PyTorch, or TensorFlow).
Proven experience designing and deploying vector databases, embeddings, and RAG architectures.
Strong knowledge of FastAPI, asyncio, and microservice architectures.
Skilled with Docker, Helm, and cloud-native stacks (AWS/Google Cloud Platform/Azure).
Excellent debugging, performance tuning, and system-level problem-solving skills.
Demonstrated ability to deliver high-quality code under pressure and within tight deadlines.
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