Al Engineer-with Knowledge Of Creating Agents Using Frontier Models - 9 Years

Al Engineer-with knowledge of creating agents using frontier models

1. Design and develop Al/ML solutions using supervised, unsupervised, deep learning, NLP, time series forecasting, and anomaly detection techniques to address business challenges.

2. Build Generative Al applications leveraging LLMs, prompt engineering, fine-tuning,

Retrieval-Augmented Generation (RAG), and Al agent frameworks.

1. Experience of working for banking domain

2. Develop and maintain end-to-end Al pipelines, covering data ingestion, preprocessing, model training, deployment, monitoring, and continuous improvement.

3. Demonstrate strong programming expertise in Python and SQL, with hands-on experience in Ml. libraries such as Scikit-learn, TensorFlow, PyTorch, Hugging Face, Spacy, and NLTK 3.

4. Work with large-scale data ecosystems, including ETL processes, data lakes, data warehouses, streaming platforms, and tools like Spark, Databricks, or Microsoft Fabric.

5. Implement MLOps best practices, including CI/CD pipelines, model governance, explainability, monitoring, Docker-based containerization, and Kubernetes orchestration.

6. Deploy Al models through APIs and microservices, ensuring seamless integration with enterprise applications, systems, and cloud platforms.

7. Utilize cloud-based Al services on Azure or AWS, including platforms such as Azure Machine Learning and Amazon SageMaker.

8. Collaborate with business and technology stakeholders to translate business requirements into scalable Al solutions while tracking ROI and value realization.

9. Contribute to Al innovation and best practices through reusable frameworks, Al copilots, semantic models, knowledge graphs, LangChain/Semantic Kernel orchestration, and synthetic data techniques