Senior / Lead Python Developer - AI / LLM / ML

Zimperium, Inc.; Texas; Hybrid; USD 150,000 - 190,000; Full time

Job summary

Zimperium, Inc. is seeking a senior AI/ML software engineer in Texas to lead production Python workloads and scalable ML solutions.

You will work with LangChain, vector databases, and modern ML frameworks, deploying containerized AI services on AWS/GCP/Azure. Candidates should have strong data architecture experience and a track record building robust REST/gRPC APIs. The role emphasizes performance, testing, and collaboration with product teams to deliver secure, reliable AI capabilities.

Qualifications

• 8+ years of production Python development experience.
• Experience with LangChain, LlamaIndex, AutoGen or Hugging Face Transformers and OpenAI/Anthropic APIs.
• Hands-on vector database experience (Pinecone, Milvus, Qdrant, Chroma, pgvector).
• Strong ML fundamentals with PyTorch or TensorFlow, scikit-learn, Pandas and NumPy.
• Experience deploying containerised AI workloads with Docker and Kubernetes on AWS/GCP/Azure.
• Data & API architecture experience with PostgreSQL, Redis, Kafka/RabbitMQ, REST/gRPC.

Skills

Python mastery

LLM & Generative AI

Vector databases

ML fundamentals

Cloud & DevOps for AI

Data & API design

Education

Bachelors or Masters in CS/Data/AI

Tools

LangChain

LlamaIndex

AutoGen

Hugging Face Transformers

Pinecone

Milvus

Qdrant

Chroma

pgvector

PyTorch

TensorFlow

scikit-learn

Pandas

NumPy

Docker

Kubernetes

AWS

GCP

Azure

PostgreSQL

Redis

Kafka

RabbitMQ

REST

gRPC

Job description

Requirements:

• Core Python Mastery: 8+ years of production experience writing clean, scalable, and maintainable Python (asyncio, design patterns, testing, performance profiling).
• LLM and Generative AI: Experience working with frameworks like LangChain, LlamaIndex, AutoGen, or Hugging Face Transformers, alongside commercial APIs (OpenAI, Anthropic).
• Vector Databases: Hands-on experience with vector search engines like Pinecone, Milvus, Qdrant, Chroma, or pgvector.
• Machine Learning Fundamentals: Strong knowledge of PyTorch or TensorFlow, scikit-learn, Pandas, NumPy, and model evaluation techniques.
• Cloud and Infrastructure: Proven experience deploying containerised AI workloads (Docker, Kubernetes) on cloud platforms (AWS, GCP, or Azure).
• Data and API Architecture: Experience with PostgreSQL, Redis, Kafka/RabbitMQ, and REST/gRPC interfaces.

Good-to-Have:

• Experience with LLM evaluation frameworks (Ragas, TruLens, Arize).
• Experience with local model serving frameworks (vLLM, Ollama, TGI).
• B. S. or M. S. in Computer Science, Data Science, Artificial Intelligence, or a related field.

Tech Stack:

• Primary Language: Python.
• AI/ML: PyTorch, Hugging Face, LangChain, LlamaIndex, vLLM.
• Databases: PostgreSQL, Redis, Qdrant / Pinecone.
• Frameworks: FastAPI, Flask, Celery.
• DevOps/MLOps: Docker, Kubernetes, MLflow, AWS/GCP.