Role - Data Engineer
Experience Required - 8+ years
Key Responsibilities:
Design, develop, and deploy Generative AI solutions, including LLM fine-tuning and RAG-based architectures for enterprise use cases.
Build and optimize end-to-end ML/DL pipelines from data ingestion to model deployment and monitoring.
Develop and fine-tune deep learning models using frameworks such as TensorFlow and PyTorch.
Apply advanced NLP techniques for tasks such as text classification, summarization, question answering, entity extraction, and conversational AI.
Implement vector databases and embeddings for semantic search and retrieval use cases.
Evaluate model performance, conduct error analysis, and continuously improve accuracy, scalability, and efficiency.
Collaborate with product managers, data engineers, and software engineers to translate business problems into AI-driven solutions.
Stay up to date with the latest research and industry trends in GenAI, LLMs, NLP, and deep learning.
Required Skills & Qualifications:
Strong foundation in Machine Learning and Deep Learning (supervised, unsupervised, and reinforcement learning concepts).
Hands-on experience with Generative AI models and LLM fine-tuning (e.g., LoRA, PEFT, prompt tuning).
Proven experience building RAG-based systems, including document ingestion, embedding generation, and retrieval pipelines.
In-depth knowledge of NLP techniques and transformer-based models (e.g., BERT, GPT, T5, LLaMA).
Proficiency in Python and ML/DL frameworks such as PyTorch or TensorFlow.
Experience with vector databases (e.g., FAISS, Pinecone, Weaviate, Milvus).
Solid understanding of model evaluation, optimization, and experimentation.
Experience with cloud platforms (AWS, Azure, or GCP) and ML deployment workflows.
Strong problem-solving, analytical, and communication skills.
Preferred / Good to Have:
Experience with MLOps tools and practices (CI/CD, model monitoring, versioning).
Knowledge of distributed training and performance optimization.
Experience working with large-scale unstructured data.
Familiarity with AI ethics, bias, and responsible AI practices.
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