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AI Tech Lead

Job in West Chester, Chester County, Pennsylvania, 19383, USA
Listing for: Omni Inclusive
Full Time position
Listed on 2026-07-01
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Job Description & How to Apply Below

Job Title

Mandatory

Skills:

Python, Flask, FastAPI, OpenAI, Lang Chain, Hugging Face Transformers, or Llama Index, SQL, Power BI, AWS Sagemaker, Azure ML, or GCP AI Platform;
Git, Docker, CI/CD

JD:
  • Experience:

    5+ years of experience in AI/ML development and Python-based solutions.
  • Domain:
    Telecom BSS & OSS domain and understanding of fixed, mobile, IoT & convergence domains and related markets
  • Business Systems (BSS)- Strong Experience in Designing E2E BSS Solutions across Sales, Marketing, Finance, Product Management, Care areas for CSPs.
  • Hands-on experience in major telecom industry B/OSS COTS (Amdocs, Netcracker, Comarch, Ericsson etc.) is must.
  • Must have knowledge around TM Forum Standard, Processes
  • Experience in 5G Network – architecture, implementation or design, thorough understanding of core concepts like network slicing
  • Programming

    Languages:

    Strong proficiency in Python (Num Py, Pandas, Scikit-learn, PyTorch, Tensor Flow).
  • Generative AI Expertise:
    Hands-on experience with OpenAI, Lang Chain, Hugging Face Transformers, or Llama Index.
  • Web Development:
    Working knowledge of Flask, FastAPI, or Django for backend application development.
  • ML Lifecycle:
    Experience in model training, evaluation, deployment, and monitoring.
  • Data Engineering:
    Exposure to data ingestion, preprocessing, and visualization (SQL, Pandas, Power BI, or Streamlit).
  • Cloud & MLOps:
    Familiarity with AWS Sagemaker, Azure ML, or GCP AI Platform;
    Git, Docker, CI/CD.

Preferred Qualifications:

  • Bachelor's or Master's degree in Computer Science, Data Science, or related field.
  • Certification or specialization in AI/ML, Deep Learning, or Generative AI is a plus.
  • Familiarity with LLM fine-tuning, RAG architecture, and prompt engineering is an advantage.
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