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Python + AI Lead

Digital Dhara LLCDallas, TX🇺🇸United StatesPosted Sep 26, 2026

Why This Role Stands Out

Lead innovative AI projects at Digital Dhara LLC, leveraging your Python expertise and production AI experience in a hybrid environment. This role is ideal for a seasoned professional seeking to drive impactful solutions and grow within a forward-thinking company. You are encouraged to apply and explore this exciting opportunity.

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
Dallas, TX, United States
Posted
1 week 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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