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Part Time Infosys Machine Learning Jobs (NOW HIRING)

John Deere

$15 - $40/hr

The Part-Time Student Program is primarily designed to augment the Company's regular full-time ... Experience with machine learning and deep learning frameworks such as PyTorch or TensorFlow.

John Deere

$15 - $40/hr

The Part-Time Student Program is primarily designed to augment the Company's regular full-time ... Experience with machine learning and deep learning frameworks such as PyTorch or TensorFlow.

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As of Sep 13, 2026, the average yearly pay for part time infosys machine learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

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Part Time Infosys Machine Learning jobs near you

Instructor, AI/Machine Learning - NLP & Audio Analytics, Simplilearn (Part time

Remote

Fullstack Academy
Colleges, Universities, and Professional Schools • 51 - 200 employees
Learn more about Fullstack Academyexternal

$50 - $55/hr

Full-time, Part-time

Re-posted 15 days ago

Job description

About: Simplilearn
Simplilearn is the world's #1 online Bootcamp provider, enabling learners around the globe with rigorous and highly specialized training offered in partnership with world-renowned universities and leading corporations. We focus on emerging technologies and skills, such as data science, cloud computing, programming, and more - that are transforming the global economy. Our training is hands-on and immersive, including live virtual classes, integrated labs and projects, 24x7 support, and a collaborative learning environment. Over two million professionals and 2000 corporate training organizations across 150 countries have harnessed our award-winning programs to achieve their career and business goals.
Simplilearn has collaborated with Fullstack Academy to leverage its widespread footprint in the US region and partnerships with Top US universities to grow internationally.
Requirements
Job Summary
We are hiring experienced NLP & Audio Analytics Trainers to deliver live online training sessions with hands-on labs and real-world projects for working professionals.
Key Responsibilities
  • Conduct live, instructor-led online classes
  • Deliver coding demos, assisted practices, and projects
  • Address learner queries and ensure engagement
Required Skills
Natural Language Processing
  • NLP fundamentals, tools, and libraries (NLTK)
  • Text preprocessing, feature engineering, and visualization
  • Text vectorization: One-Hot, BoW, TF-IDF
  • Sentiment analysis using Naive Bayes
  • Word embeddings: Word2Vec, GloVe
  • Machine Translation and document search (TF-IDF, BM25)
  • Sequence models (RNNs) and challenges
  • Attention models, Transformers, BERT, GPT
Audio Analytics & Speech
  • Audio processing using Python (Librosa, PyDub)
  • Audio visualization and feature extraction
  • Digital Signal Processing and MFCCs
  • Deep Learning for speech recognition
  • Transfer learning for speech models
  • Audio synthesis and music generation using GANs

Working Hours -
The total weekly part-time commitment is expected to be 8-12 hours.
Weekday - 06:30 PM CST - 09:30 PM CST
Weekend - 09:00 AM CST - 12:00 PM CST
Class Timings -
For Weekend sessions, 6-8 hrs of delivery
- For Weekday sessions, 9-12 hrs of delivery
- 1 prep hour/session for the first cohort and 0.5 prep hour per session for the future cohorts
- If content revamped, prep can be at 1 hour/session for the first batch and 0.5 hour per session for the future batches
***Feedback communication/General connects Minutes/hours are not billable
***Prep hour can be billed only for the actual scheduled teaching hours(Induction sessions excluded)
Qualifications
  • 10+ years of hands-on experience in NLP and/or Audio Analytics
  • Strong Python and ML/DL skills
  • Prior online/classroom training experience

Benefits
Compensation:
The expected compensation for this role for candidates from United States is $50 -$55 per hour for candidates who fulfill the qualifications for the role. Candidates whose qualifications are above those listed are encouraged to apply as well. All final offers to candidates will be based on that candidate's unique experience and skillset, and not all candidates will qualify for the top of the salary range.
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