Python Developer with AI

Lorven Technologies Inc
Southlake, TX, United States
1 day ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Airflow Amazon Web Services Application Integration Architecture Microsoft Azure Cloud Computing Encodings Continuous Integration Python (Programming Language) Software Engineering Data Streaming Data Processing
+18 more
Google Cloud Flask (Web Framework) Large Language Models Multi-Agent Systems Prompt Engineering Apache Spark Model Validation Backend Git Fastapi Containerization Kubernetes Apache Kafka Machine Learning Operations Restful APIs Software Version Control Data Pipelines Docker

Job description

Our client is looking for an Python Developer with AI exp in Southlake, TX, below is the detailed requirement.

Requirements

5+ years of professional software development experience, with strong, current Python expertise. Demonstrable experience delivering at least one AI/ML or LLM-powered system to production and supporting it through its life cycle. Practical familiarity with the AI Development Life Cycle (AIDLC): data handling, experimentation, evaluation, deployment, and monitoring. Hands-on experience with LLM application development - prompt engineering, RAG, and integrating APIs such as those from major model providers. Proficiency designing and consuming RESTful APIs and building scalable backend services. Working knowledge with cloud platforms (AWS, Google Cloud Platform, or Azure) and containerization (Docker). Solid grounding with version control (Git) and CI/CD workflows. Strong communication skills and the ability to work across engineering, data, and product teams. Experience with agentic frameworks and orchestration (e.g., LangChain, LangGraph, LlamaIndex, or multi-agent frameworks). Familiarity with vector databases (e.g., Pinecone, Weaviate, pgvector, FAISS) and embedding-based retrieval. Exposure to MLOps/LLMOps tooling - MLflow, Weights & Biases, model registries, and feature stores. Experience with Kubernetes and infrastructure-as-code. Understanding of model evaluation, responsible AI, safety guardrails, and observability for LLM systems. Background with data pipelines and streaming (e.g., Kafka, Spark, or Airflow). Python · FastAPI / Flask · LLM & Agentic frameworks · Vector databases · Docker · CI/CD · Cloud (AWS / Google Cloud Platform / Azure) · MLOps/LLMOps tooling

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Good distractions

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Favorite git commands and the importance of patch commits

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