Job Description :

Key Responsibilities :

- Design and develop machine learning models, deep learning architectures, and AI solutions to address business challenges.

- Build end-to-end ML pipelines including data ingestion, feature engineering, model training, evaluation, and deployment.

- Develop and integrate RESTful APIs and microservices to serve AI/ML models in production with high availability.

- Implement generative AI solutions leveraging LLMs, RAG, prompt engineering, and fine-tuning.

- Collaborate with cross-functional teams to translate research prototypes into scalable applications.

- Optimize model performance through hyperparameter tuning, A/B testing, and continuous monitoring.

- Architect scalable AI infrastructure using cloud-native services (Azure AI, AWS SageMaker, or GCP Vertex AI) and containerization (Docker, Kubernetes).

- Maintain MLOps practices including version control, automated retraining, and CI/CD workflows.

- Ensure responsible AI practices including fairness, explainability, and bias detection.

Qualifications :

- Bachelor's degree in Computer Science, AI, Data Science, or related field (Required).

- Master's degree or Ph.D. (Preferred).

- Hands-on experience building and deploying ML models in production.

- Experience with end-to-end ML pipelines and MLOps in cloud environments.

Tech Stack :

- Python, TensorFlow, PyTorch, scikit-learn, Hugging Face, LangChain.

- Azure ML, AWS SageMaker, GCP Vertex AI.

- Pandas, NumPy, Spark, SQL.

- MLflow, Kubeflow, Docker, Kubernetes.

- GPT, Claude, LLaMA, Vector Databases (Pinecone, Weaviate, FAISS).