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HCLTech Off Campus Drive 2025 Details :
HCLTech Off Campus Drive 2025 Eligibility Criteria :
Work Experience & Qualification criteria:
- It is essential to have a solid understanding of data structures, algorithms, and principles of software engineering.
- Experience with vector databases RDBMS, MongoDB and NoSQL databases.
- Proficiency in working with embeddings.
- Strong distributed systems skills and system architecture skills
- Experienced in building and running a large platform at scale.
- Hands-on experience with Python, Hugging Face, TensorFlow, Keras, PyTorch, Spark, or similar statistical tools.
- Experience as data modeling ML/NLP scientist. including, but not limited to, Performance tuning, fine-tuning, RLHF, and performance optimization. Validated background with ML toolkits, such as PyTorch, TensorFlow, Keras, Langchain, Llamadindex, SparkML, or Databricks.
- Proficient with the integration of data from multiple data sources Experience with NoSQL databases, such as HBase, ElasticSearch, and MongoDB API Design. API/Data mapping to schema.
- Experienced in and strong knowledge of using AI/ML and more particularly LLMs eager to apply this rapidly changing technology.
- Good Knowledge of Kubernetes, and RESTful design.
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Job Description :
- Proficient in Python, with experience in machine learning, deep learning, and NLP processing.
- Experience in developing and implementing generative AI models, with a strong understanding of deep learning techniques such as GPT, VAE, and GANs.
- Proficient in Langchain, LLM
- Prompt Engineering: The engineer prompts and optimizes few-shot techniques to enhance LLM’s performance on specific tasks, e.g. personalized recommendations.
- Model Evaluation & Optimization: Evaluate LLM’s zero-shot and few-shot capabilities, fine-tuning hyperparameters, ensuring task generalization, and exploring model interpretability for robust web app integration.
- Response Quality: Collaborate with ML and Integration engineers to leverage LLM’s pre-trained potential, delivering contextually appropriate responses in a user-friendly web app.
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