Python + AI Lead
Cloudious LLC
Dallas, TX, United States
3 days 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
LangGraph Framework
Artificial Intelligence
Airflow
Amazon Web Services
Application Integration Architecture
Microsoft Azure
Cloud Computing
Encodings
Continuous Integration
Python (Programming Language)
Software Engineering
Data Streaming
+27 more
Pinecone
Feature Store
Data Processing
LangChain
Retrieval-Augmented Generation
Flask (Web Framework)
Large Language Models
Multi-Agent Systems
Prompt Engineering
Apache Spark
Model Validation
Llamaindex
Backend
Agentic-AI
Git
Fastapi
Pgvector
Containerization
Kubernetes
Weaviate
Apache Kafka
Machine Learning Operations
FAISS
Restful APIs
Software Version Control
Data Pipelines
Docker
Requirements
- 10+ Years of overall experience and 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 in designing and consuming RESTful APIs and building scalable backend services.
- Working knowledge of cloud platforms (AWS, GCP, or Azure) and containerization (Docker).
- Solid grounding in 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).
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Python FastAPI / Flask LLM & Agentic frameworks Vector databases Docker CI/CD Cloud (AWS / GCP / Azure) MLOps/LLMOps tooling
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Prepare application
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