Social Good & Community Development
Full-Time
Applore Technologies
Overview
Key Responsibilities
Machine Learning (ML) :
Machine Learning (ML) :
- Design, build, and deploy ML pipelines for structured and unstructured data.
- Conduct feature engineering, model selection, and hyperparameter tuning.
- Evaluate model performance using industry-standard metrics and improve accuracy.
- Collaborate with data engineering teams to ensure clean and accessible data for model training.
- Apply supervised, unsupervised, and reinforcement learning techniques as per use case.
- Develop NLP pipelines for text classification, sentiment analysis, entity recognition, summarization, etc.
- Leverage libraries like spaCy, NLTK, Hugging Face Transformers, and Gensim.
- Preprocess and tokenize large corpora using advanced NLP methods.
- Implement solutions for multi-lingual, domain-specific text data challenges.
- Integrate NLP services with applications or workflows.
- Fine-tune and deploy LLMs (e.g., GPT, LLaMA, BERT, Falcon, Mistral) on custom datasets.
- Use prompt engineering and retrieval augmented generation (RAG) to build intelligent systems.
- Optimize inference performance and latency for production-level deployment.
- Stay updated with the latest in GenAI, foundation models, and transformer architectures.
- Work on use cases like chatbots, question answering, summarization, content generation, and semantic search.
- Proficiency in Python, PyTorch or TensorFlow, and data science libraries (scikit-learn, pandas,
- Experience with NLP frameworks and transformer-based architectures.
- Exposure to LLM model training/fine-tuning using tools like Hugging Face, LangChain, or OpenAI API.
- Familiarity with MLOps, model versioning, and deployment using Docker/Kubernetes.
- Understanding of vector databases (e.g., FAISS, Pinecone, Weaviate) and embeddings.
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