AI/ML Architect- Healthcare- W2 at JP Techno Park
Louisville, Kentucky, USA -
Full Time


Start Date

Immediate

Expiry Date

04 Dec, 25

Salary

62.0

Posted On

06 Sep, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Python, Scikit Learn, Software Engineers, Kafka, Production Systems, Apache Spark, Domain Experience, Models, Azure, Deep Learning, Data Structures, Software Development, Nlp, Machine Learning, Reinforcement Learning, Artificial Intelligence, Airflow, Scalability, Ml

Industry

Information Technology/IT

Description

NOTE: PRIOR EXPERIENCE WORKING WITH HEALTH PLAN APPLICATIONS AND HEALTHCARE DATA ARCHITECTURES IS REQUIRED. THIS POSITION WOULD REQUIRE CANDIDATES WITH A DATA ENGINEERING BACKGROUND WHO HAVE BEEN EXTENSIVELY WORKING IN THE AI/ML SPACE FOR THE LAST 4-5 YEARS, AND WHO TOO IN THE HEALTH PLAN DOMAIN.

Job Summary:
We are seeking a highly skilled and motivated Machine Learning / AI Engineer to join our team. You will be responsible for designing, developing, and deploying machine learning models and AI-driven solutions that solve real-world problems and drive measurable business value. This role requires a strong foundation in data science, machine learning engineering, and software development, along with a passion for innovation and problem-solving.

Key Responsibilities:

  • Design and implement machine learning models for classification, regression, clustering, recommendation, NLP, or computer vision tasks.
  • Collaborate with data scientists, software engineers, and product teams to integrate ML models into production systems.
  • Build and maintain scalable data pipelines and model training workflows.
  • Conduct experiments, evaluate model performance, and iterate to improve accuracy, efficiency, and robustness.
  • Stay up to date with the latest research and advancements in AI/ML and apply them to relevant projects.
  • Optimize models for performance, scalability, and interpretability.
  • Document processes, models, and systems to ensure reproducibility and knowledge sharing.

Required Qualifications:

  • Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
  • 8+ years of experience in developing and deploying machine learning models and AI solutions in real-world environments.
  • 5+ years of experience programming in Python, with expertise in ML libraries such as TensorFlow, PyTorch, Scikit-learn, etc.
  • 5+ years of experience working with core machine learning algorithms, data structures, and statistical modeling techniques.
  • 3+ years of experience using cloud platforms (AWS, GCP, Azure) for building and deploying AI/ML solutions, including familiarity with ML Ops tools (e.g., SageMaker, Vertex AI, Azure ML).
  • 2+ years of experience or exposure to data engineering tools such as Apache Spark, Airflow, or Kafka (preferred but not mandatory).
  • Strong problem-solving, analytical thinking, and communication skills with the ability to translate complex concepts into practical applications.

Preferred Qualifications:

  • PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
  • Experience with deep learning, reinforcement learning, or generative AI (e.g., GANs, LLMs).
  • Contributions to open-source ML/AI projects or published academic research papers.
  • Experience deploying models in real-time inference systems or on edge devices.
  • Prior experience working with Health Plan applications and healthcare data architectures is a strong plus.

Job Type: Contract
Pay: $60.00 - $62.00 per hour
Expected hours: 40 per week
People with a criminal record are encouraged to apply
Work Location: Remot

Responsibilities
  • Design and implement machine learning models for classification, regression, clustering, recommendation, NLP, or computer vision tasks.
  • Collaborate with data scientists, software engineers, and product teams to integrate ML models into production systems.
  • Build and maintain scalable data pipelines and model training workflows.
  • Conduct experiments, evaluate model performance, and iterate to improve accuracy, efficiency, and robustness.
  • Stay up to date with the latest research and advancements in AI/ML and apply them to relevant projects.
  • Optimize models for performance, scalability, and interpretability.
  • Document processes, models, and systems to ensure reproducibility and knowledge sharing
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