Lead Analyst, AI Engineer (Data Science)
at Semiconductor Manufacturing Industry
Python
Machine Learning (ML)
Deep Learning
Elastic Search
Git
GitHub
DockeršÆ 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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