Applied AI/ML Engineer
INR 2500K-4000K (estimate) Mid-level Full Time
Tasks
- Build end-to-end ML pipelines
- Collaborate with data engineering platform and product teams
- Deploy and manage training and inference on Kubernetes
- Design develop fine tune and evaluate NLP models
- Develop APIs and microservices for model inference
- Ensure data privacy, security, and governance compliance
- Implement LLM based solutions with prompt engineering
- Implement RAG solutions
- Implement automated retraining
- Monitor model performance and data drift
- Set up CI CD pipelines for model improvement
- Translate business requirements into AI/ML solutions
Perks/Benefits
Skills/Tech-stack
Airflow | BLEU | CI/CD | Dask | Data Governance | Data Privacy | Embeddings | F1-score | Faiss | FastAPI | Feature Store | Federated Learning | Fine Tuning | GDPR | GPU | Grafana | HIPAA | Hugging Face | Hugging Face Transformers | Inference Server | KServe | Kubeflow | Kubernetes | Langchain | Langgraph | Llamaindex | ML practices | MLflow | NLTK | NVIDIA Triton | NVIDIA Triton Inference | NVIDIA Triton Inference Server | OpenAI API | Pinecone | Precisión | Prometheus | Prompt engineering | PyTorch | Python | ROC-AUC | Ray | Recall | Retrieval-Augmented Generation | Scikit-learn | Secure ML | Secure ML Practices | Spacy | Spark | TensorFlow | Torchserve | Triton Inference Server | Vector Databases | Weaviate
Education
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