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AI ML Data Engineer

AI ML Data Engineer

KLA

0 - 5 years

15 - 20 Lacs

chennai

Posted:7 hours ago| Platform:

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Skills Required

data engineer numpy sql docker deep learning tensorflow git model evaluation elasticsearch gcp pytorch keras data wrangling ml azure chatbot python github ai llm machine learning pandas regression feature engineering aws

Work Mode

Work from Office

Job Type

Full Time

Job Description

Job Description/Preferred Qualifications

Role You will be part of a cutting-edge team working on Large Language Models (LLMs), Machine Learning, Deep Learning, and Retrieval-Augmented Generation (RAG) pipelines. Youll help design, build, and evaluate AI systems that solve complex real-world problems at scale. Key Responsibilities Develop and optimize RAG pipelines: document chunking, embedding generation, vector storage, retrieval, reranking, and grounded generation with citations. Work on LLM-based applications: fine-tuning open-source models (LLaMA, Mistral, etc.), building prompt strategies, and deploying inference services. Contribute to machine learning models (classification, regression, recommendation, anomaly detection) and deep learning architectures (CNNs, RNNs, Transformers). Implement robust model evaluation frameworks (accuracy, F1, BLEU, perplexity, hallucination detection, relevance). Collaborate with senior engineers on scalable pipelines, guardrails, and integration with enterprise systems.

Required Skills

Programming & Foundations Strong in Python, data structures, and algorithms. Hands-on with NumPy, Pandas, Scikit-learn for ML prototyping. Machine Learning Understanding of supervised/unsupervised learning, regularization, feature engineering, model selection, cross-validation, ensemble methods (XGBoost, LightGBM). Deep Learning Proficiency with PyTorch (preferred) or TensorFlow/Keras. Knowledge of CNNs, RNNs, LSTMs, Transformers, Attention mechanisms. Familiarity with optimization (Adam, SGD), dropout, batch norm. LLMs & RAG Hugging Face Transformers (tokenizers, embeddings, model fine-tuning). Vector databases (Milvus, FAISS, Pinecone, ElasticSearch). Prompt engineering, function/tool calling, JSON schema outputs. Data & Tools SQL fundamentals; exposure to data wrangling and pipelines. Git/GitHub, Jupyter, basic Docker.

Nice to Have

Built a personal ML/LLM project (chatbot, RAG app, document Q&A, finetuned model). Familiarity with LangChain/LlamaIndex/Agno frameworks. Knowledge of cloud platforms (Azure/AWS/GCP) and MLOps basics (CI/CD, MLflow, W&B). Exposure to knowledge graphs or multi-agent workflows.

What Were Looking For

Solid academic foundation with strong applied ML/DL exposure. Curiosity to learn cutting-edge AI and willingness to experiment. Clear communicator who can explain ML/LLM trade-offs simply. Strong problem-solving and ownership mindset.

Minimum Qualifications

Doctorate (Academic) Degree and 0 years related work experience; Master's Level Degree and related work experience of 3 years; Bachelor's Level Degree and related work experience of 5 years

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AI ML Data Engineer