Software Engineer (AI/ML)
Tasks
- Apply prompt engineering
- Build and fine-tune large language models
- Build vector database semantic search
- Conduct AI research and experimentation
- Deploy ML models on cloud or on prem infrastructure
- Design generative AI models
- Develop NLP algorithms
- Develop conversational AI solutions
- Evaluate model performance
- Implement RAG architecture
- Integrate AI components into production pipelines
- Perform hyperparameter tuning
- Process and analyze large datasets
- Train and optimize machine learning models
Perks/Benefits
- Birthday day off
- Career advancement
- Casual leave
- Employee assistance program
- Learning and development opportunities
- Medical insurance
- Paid Holidays
- Paid Maternity Leave
- Paid sick leave
- Paternity leave
- Provident fund and gratuity
- Wellness initiatives
- Work life balance leaves
Skills/Tech-stack
AWS | Attention Mechanisms | Azure | Cloud platform | Deployment Pipelines | Domain Adaptation | Embeddings | Entity recognition | Fine Tuning | GAN | GPT | Generative AI | Google Cloud | Google Cloud Platform | Hugging Face | Hugging Face Transformers | Hyperparameter Tuning | Keras | LSTM | Langchain | Language Models | Language Processing | Large Language Models | Llama | MLOps | Machine Learning | Model Compression | Model Deployment | Model deployment pipelines | NLTK | Named Entity Recognition | Natural Language | Natural Language Processing | NumPy | Pandas | Prompt engineering | PySpark | PyTorch | Python | RAG | Retrieval-Augmented Generation | SciPy | Scikit-learn | Semantic Search | Sentiment Analysis | Sequence Modeling | Spacy | TensorFlow | Text Classification | Tokenization | Transfer Learning | Transformers | VAE | Vector Databases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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