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Lead Machine Learning Engineer

NITYA Software Solutions, Inc.
Fort Lauderdale, FL, United States
11 days ago
Apply on www.dice.com
Prepare application

Role details

Contract type
Temporary to permanent
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
3 years minimum
Working hours
Regular working hours
Job source
www.dice.com

Tech stack

Artificial Intelligence Python (Programming Language) Machine Learning Recommender Systems Tensorflow Pytorch Scikit Learn Machine Learning Operations Databricks

Requirements

  • They are seeking an experienced Lead Machine Learning Engineer to lead the development and operationalization of machine learning solutions that power Customer 360, recommendation engines, customer intelligence, personalization, and next-generation AI experiences.
  • The ideal candidate has successfully built recommendation engines, customer prediction models, and AI-driven solutions in highly scalable customer-centric environments.
  • 8+ years of Machine Learning Engineering or Applied AI experience.
  • 3+ years in a Lead, Principal, or Senior Technical Leadership role.
  • Proven experience delivering production-grade machine learning systems.
  • Strong expertise with: Databricks, Mlflow, Python, Machine Learning Frameworks (TensorFlow, PyTorch, Scikit-learn), Real-time inference architectures
  • Experience building: Recommendation systems, Personalization engines, Predictive analytics solutions, and Customer intelligence platforms
  • Pluses:
  • Customer 360 implementation experience.
  • Identity Graph experience.
  • Hospitality, Gaming, Entertainment, Retail, Loyalty, or Consumer Digital experience.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.dice.com
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

Moments

Explore playlists

2:35 min

Preventing remote code execution in PyTorch models

Balázs Kiss · World Congress 2023

1:39 min

Fundamentals of tensors and the TensorFlow library

Håkan Silfvernagel · LIVE

2:27 min

Managing traffic and tracking costs with Databricks Unity Catalog

Viktoria Semaan Viktoria Semaan · World Congress 2026 Europe

3:03 min

Career evolution in data engineering and AI platforms

Maria Apazoglou · Coffee With Developers

1:06 min

Compiling PyTorch environments for advanced time forecasting

Christoph Lohrmann Christoph Lohrmann +1 · World Congress 2026 Europe

1:33 min

Summary of machine learning capabilities and engineering opportunities

Jan Zawadzki · LIVE

Videos

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How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale) thumbnail
27:23
World Congress 2023 • almost 3 years ago

How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)

Dora Petrella

Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding) thumbnail
29:32
World Congress 2026 Europe • about 2 months ago

Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)

Christoph Lohrmann, Utz Bacher

Machine learning in the browser with TensorFlowjs thumbnail
38:24
LIVE • over 5 years ago

Machine learning in the browser with TensorFlowjs

HÃ¥kan Silfvernagel

Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source thumbnail
29:50
World Congress 2026 Europe • about 2 months ago

Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source

Viktoria Semaan

Getting Started with Machine Learning thumbnail
44:11
LIVE • almost 5 years ago

Getting Started with Machine Learning

Alexandra Waldherr

Data Science in Retail thumbnail
58:21
LIVE • over 3 years ago

Data Science in Retail

Julian Joseph

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