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Semiconductor Manufacturing Industry

Semiconductor Manufacturing Industry

Agency job
via Peak Hire Solutions by Dharati Thakkar

Chennai · 5 - 8 years · ₹40L - ₹48L / yr · Posted 31 Oct 2025

skill iconPython
skill iconMachine Learning (ML)
Image Processing
skill iconDeep Learning
Algorithms
+28 more

🎯 Ideal Candidate Profile:

This role requires a seasoned engineer/scientist with a strong academic background from a premier institution and significant hands-on experience in deep learning (specifically image processing) within a hardware or product manufacturing environment.


📋 Must-Have Requirements:

Experience & Education Combinations:

Candidates must meet one of the following criteria:

  • Doctorate (PhD) + 2 years of related work experience
  • Master's Degree + 5 years of related work experience
  • Bachelor's Degree + 7 years of related work experience


Technical Skills:

  • Minimum 5 years of hands-on experience in all of the following:
  • Python
  • Deep Learning (DL)
  • Machine Learning (ML)
  • Algorithm Development
  • Image Processing
  • 3.5 to 4 years of strong proficiency with PyTorch OR TensorFlow / Keras.


Industry & Institute:

  • Education: Must be from a premier institute (IIT, IISC, IIIT, NIT, BITS) or a recognized regional tier 1 college.
  • Industry: Current or past experience in a Product, Semiconductor, or Hardware Manufacturing company is mandatory.
  • Preference: Candidates from engineering product companies are strongly preferred.


ℹ️ Additional Role Details:

  • Interview Process: 3 technical rounds followed by 1 HR round.
  • Work Model: Hybrid (requiring 3 days per week in the office).


Based on the job description you provided, here is a detailed breakdown of the Required Skills and Qualifications for this AI/ML/LLM role, formatted for clarity.


📝 Required Skills and Competencies:

💻 Programming & ML Prototyping:

  • Strong Proficiency: Python, Data Structures, and Algorithms.
  • Hands-on Experience: NumPy, Pandas, Scikit-learn (for ML prototyping).


🤖 Machine Learning Frameworks:

  • Core Concepts: Solid understanding of:
  • Supervised/Unsupervised Learning
  • Regularization
  • Feature Engineering
  • Model Selection
  • Cross-Validation
  • Ensemble Methods: Experience with models like XGBoost and LightGBM.


🧠 Deep Learning Techniques:

  • Frameworks: Proficiency with PyTorch OR TensorFlow / Keras.
  • Architectures: Knowledge of:
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Long Short-Term Memory networks (LSTMs)
  • Transformers
  • Attention Mechanisms
  • Optimization: Familiarity with optimization techniques (e.g., Adam, SGD), Dropout, and Batch Normalization.


💬 LLMs & RAG (Retrieval-Augmented Generation):

  • Hugging Face: Experience with the Transformers library (tokenizers, embeddings, model fine-tuning).
  • Vector Databases: Familiarity with Milvus, FAISS, Pinecone, or ElasticSearch.
  • Advanced Techniques: Proficiency in:
  • Prompt Engineering
  • Function/Tool Calling
  • JSON Schema Outputs


🛠️ Data & Tools:

  • Data Management: SQL fundamentals; exposure to data wrangling and pipelines.
  • Tools: Experience with Git/GitHub, Jupyter, and basic Docker.


🎓 Minimum Qualifications (Experience & Education Combinations):

Candidates must have experience building AI systems/solutions with Machine Learning, Deep Learning, and LLMs, meeting one of the following criteria:

  • Doctorate (Academic) Degree + 2 years of related work experience.
  • Master's Level Degree + 5 years of related work experience.
  • Bachelor's Level Degree + 7 years of related work experience.


⭐ Preferred Traits and Mindset:

  • Academic Foundation: Solid academic background with strong applied ML/DL exposure.
  • Curiosity: Eagerness to learn cutting-edge AI and willingness to experiment.
  • Communication: Clear communicator who can explain ML/LLM trade-offs simply.
  • Ownership: Strong problem-solving and ownership mindset.
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