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Python + AI Lead

Digital Dhara LLCDallas, TX🇺🇸United StatesPosted 16 Sept 2026

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
Dallas, TX, United States
Posted
15 hours ago
DockerAWSMLOpsMLflowAirflowAzureGitGoogle CloudKafkaKubernetesLLMPython

Job Description

Position: Python + AI Lead

Location: Dallas, TX (Hybrid)

 

Minimum Qualifications

 

                      10+ Years of overall exp 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 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.

 

Preferred Qualifications

                      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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