Lead Analyst, AI Engineer (Data Science)

🎯 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 experienceMaster's Degree + 5 years of related work experienceBachelor's Degree + 7 years of related work experienceTechnical Skills:Minimum 5 years of hands-on experience in all of the following:PythonDeep Learning (DL)Machine Learning (ML)Algorithm DevelopmentImage Processing3.5 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 LearningRegularizationFeature EngineeringModel SelectionCross-ValidationEnsemble 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)TransformersAttention MechanismsOptimization: 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 EngineeringFunction/Tool CallingJSON 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.