Lead Analyst, AI Engineer (Data Science)

Semiconductor Manufacturing Industry

Lead Analyst, AI Engineer (Data Science)

Peak Hire Solutions

Company

Home

Lead Analyst, AI Engineer (Data Science)

at Semiconductor Manufacturing Industry

Agency job

via Peak Hire Solutions

5 - 8 yrs

₹40L - ₹48L / yr

Chennai

Skills

Python

Machine Learning (ML)

Image Processing

Deep Learning

Algorithms

PyTorch

TensorFlow

Keras

Data Structures

NumPy

pandas

Scikit-Learn

Prototyping

XGBoost

Recurrent neural network (RNN)

Long short-term memory (LSTM)

Transformer

Mechanics

Adams Software

Normalization

Batch processing

Large Language Models (LLM)

Retrieval Augmented Generation (RAG)

Vector database

Huggingface

Elastic Search

Prompt engineering

JSON

SQL

Data wrangling

Git

GitHub

Docker

🎯 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.

🎯 Ideal Candidate Profile:

📋 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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