Lead Analyst, AI Engineer (Data Science)
Semiconductor Manufacturing Industry
Lead Analyst, AI Engineer (Data Science)
at Semiconductor Manufacturing Industry
Agency job
5 - 8 yrs
ā¹40L - ā¹48L / 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
šÆ 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.
š 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.
ā¹ļø 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.
š Required Skills and Competencies:
š» Programming & ML Prototyping:
- Strong Proficiency: Python, Data Structures, and Algorithms.
- Hands-on Experience: NumPy, Pandas, Scikit-learn (for ML prototyping).
š¤ 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.
š§ 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.
š¬ 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
š ļø Data & Tools:
- Data Management: SQL fundamentals; exposure to data wrangling and pipelines.
- Tools: Experience with Git/GitHub, Jupyter, and basic Docker.
š 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.
ā 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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