Software Engineer
INR 3000K-4500K (estimate) Senior-level Full Time
Tasks
- Architect end-to-end AI/ML pipelines
- Build RAG pipelines with vector stores and document parsing
- Build data pipelines for training and evaluation
- Collaborate with stakeholders on AI ML opportunities
- Conduct research on deep learning NLP time series and computer vision
- Deploy models to cloud and on premise environments
- Design develop deploy AI/ML models
- Ensure AI ethics data privacy and AI governance compliance
- Ensure data quality and preprocessing best practices
- Implement model monitoring versioning and re training with MLOps
- Integrate AI into existing systems
- Integrate multimodal conversational AI platforms
- Optimize models for performance latency and scalability
Perks/Benefits
Skills/Tech-stack
API Development | Amazon Lex | Amazon SageMaker | Amazon Web Services | Apache Airflow | Apache Kafka | Apache Spark | Azure Machine Learning | CCPA | Computer Vision | Continuous Delivery | Continuous Integration/Continuous Delivery | Continuous integration | Data Preprocessing | Data Privacy | Deep learning | Dimensionality Reduction | Docker | Document parsing | Drift Detection | Feature Engineering | GDPR | Google Cloud | Google Cloud Vertex | Google Cloud Vertex AI | Google Dialogflow | Kubernetes | Language Models | Language Processing | Large Language Models | MLOps | MLflow | Machine Learning | Microsoft Azure | Model Drift | Model Drift Detection | Model Monitoring | Model Serving | Model versioning | Multimodal AI | Natural Language | Natural Language Processing | ONNX | PyTorch | Python | Rasa | Reinforcement Learning | Retrieval-Augmented Generation | Scikit-learn | Secure ML | Semantic Search | Series forecasting | Statistical modeling | TensorFlow | TensorFlow Serving | Time Series | Time Series Forecasting | Vector Databases | Vertex AI | Web Services | XGBoost
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI/ML Engineer | Engineer | ML Engineer
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