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AI/ML Lead

Noida, Uttar Pradesh, India

Live, first seen by us 22 days ago
Pay
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Work setting
On-site — work setting source
Work setup in the employer’s posting: On-site
From the employer’s posting
Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
Apply at Training Basket

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Status in our records
Live
First seen by us
Oct 22, 202622 days ago
Recorded sightings
35
Last seen by us
Nov 13, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 22

Similar jobs usually come down between day 8 and day 76.

From 5,460 similar postings that came down. Coming down is not always a hire.

Training Basket is hiring

6 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • SQL
  • AWS
  • Azure
  • Docker
  • Kubernetes
  • FastAPI
  • Flask
  • NumPy
  • pandas
  • PyTorch
  • scikit-learn
  • TensorFlow
  • GCP
Where the posting names them
Lead the design, development, testing, and deployment of AI/ML models and solutions. Define AI/ML strategies and technical roadmaps aligned with business objectives. Build and optimize machine learning and deep learning models for real-world applications. Oversee data preprocessing, feature engineering, model training, validation, and evaluation. Work with algorithms for classification, regression, clustering, and predictive analytics. Explore and implement Generative AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) solutions. Develop scalable AI pipelines and production-ready ML applications. Implement MLOps practices for model deployment, monitoring, versioning, and retraining. Collaborate with data engineers, software developers, product managers, and business stakeholders. Mentor and guide AI/ML engineers and ensure high-quality technical delivery. Monitor model performance, accuracy, scalability, and reliability. Ensure data privacy, model security, responsible AI practices, and compliance. Evaluate emerging AI technologies and recommend suitable tools and frameworks. Communicate project progress, technical insights, and business impact to stakeholders. Requirements Strong programming skills in Python. Expertise in machine learning libraries such as Scikit-learn, TensorFlow, and PyTorch. Strong understanding of statistics, probability, linear algebra, and optimization. Experience with data processing and analysis using Pandas, NumPy, and SQL. Knowledge of deep learning, NLP, and computer vision. Experience with Generative AI, LLMs, prompt engineering, and RAG architectures. Familiarity with frameworks such as LangChain or LlamaIndex. Experience with model deployment using FastAPI, Flask, or REST APIs. Knowledge of MLOps tools, Docker, Kubernetes, and CI/CD pipelines. Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud. Strong leadership, problem-solving, communication, and project management skills.

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Demand for ML Engineer roles

New ML Engineer postings at the employers we follow are up 10% over the last 6 weeks compared with the 6 before.

Weekly new ML Engineer postings at the same employers, from the week of August 17 to the week of November 2. The fewest in a week was 612, the most 1,081, and the latest week had 1,068.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 6 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

Lead the design, development, testing, and deployment of AI/ML models and solutions. Define AI/ML strategies and technical roadmaps aligned with business objectives. Build and optimize machine learning and deep learning models for real-world applications. Oversee data preprocessing, feature engineering, model training, validation, and evaluation. Work with algorithms for classification, regression, clustering, and predictive analytics. Explore and implement Generative AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) solutions. Develop scalable AI pipelines and production-ready ML applications. Implement MLOps practices for model deployment, monitoring, versioning, and retraining. Collaborate with data engineers, software developers, product managers, and business stakeholders. Mentor and guide AI/ML engineers and ensure high-quality technical delivery. Monitor model performance, accuracy, scalability, and reliability. Ensure data privacy, model security, responsible AI practices, and compliance. Evaluate emerging AI technologies and recommend suitable tools and frameworks. Communicate project progress, technical insights, and business impact to stakeholders. Requirements Strong programming skills in Python. Expertise in machine learning libraries such as Scikit-learn, TensorFlow, and PyTorch. Strong understanding of statistics, probability, linear algebra, and optimization. Experience with data processing and analysis using Pandas, NumPy, and SQL. Knowledge of deep learning, NLP, and computer vision. Experience with Generative AI, LLMs, prompt engineering, and RAG architectures. Familiarity with frameworks such as LangChain or LlamaIndex. Experience with model deployment using FastAPI, Flask, or REST APIs. Knowledge of MLOps tools, Docker, Kubernetes, and CI/CD pipelines. Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud. Strong leadership, problem-solving, communication, and project management skills.
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Noida, Uttar Pradesh, India

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