Senior AI Engineer

Infosys Pontoon•New York, NY

6d•Hybrid

About The Position

We are seeking a highly skilled Senior AI Engineer with a minimum of 5 years of relevant experience and 10 years of overall experience. This role involves designing and developing AI/ML solutions, building Generative AI applications, and managing end-to-end AI pipelines. The ideal candidate will have strong programming expertise in Python and SQL, experience with large-scale data ecosystems, and a solid understanding of MLOps best practices. This position will collaborate with stakeholders to translate business requirements into scalable AI solutions and contribute to AI innovation.

Requirements

Minimum of 5 years of relevant experience; 10 years of overall experience.
Strong programming expertise in Python and SQL.
Hands-on experience in ML libraries such as Scikit-learn, TensorFlow, PyTorch, Hugging Face, SpaCy, and NLTK.
Experience with large-scale data ecosystems, including ETL processes, data lakes, data warehouses, streaming platforms, and tools like Spark, Databricks, or Microsoft Fabric.
Experience with MLOps best practices, including CI/CD pipelines, model governance, explainability, monitoring, Docker-based containerization, and Kubernetes orchestration.
Experience deploying AI models through APIs and microservices.
Experience utilizing cloud-based AI services on Azure or AWS, including platforms such as Azure Machine Learning and Amazon SageMaker.
Experience collaborating with business and technology stakeholders to translate business requirements into scalable AI solutions.

Nice To Haves

Experience of working for banking domain
Java Springboot
Angular

Responsibilities

Design and develop AI/ML solutions using supervised, unsupervised, deep learning, NLP, time series forecasting, and anomaly detection techniques to address business challenges.
Build Generative AI applications leveraging LLMs, prompt engineering, fine-tuning, Retrieval-Augmented Generation (RAG), and AI agent frameworks.
Develop and maintain end-to-end AI pipelines, covering data ingestion, preprocessing, model training, deployment, monitoring, and continuous improvement.
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.
Work with large-scale data ecosystems, including ETL processes, data lakes, data warehouses, streaming platforms, and tools like Spark, Databricks, or Microsoft Fabric.
Implement MLOps best practices, including CI/CD pipelines, model governance, explainability, monitoring, Docker-based containerization, and Kubernetes orchestration.
Deploy AI models through APIs and microservices, ensuring seamless integration with enterprise applications, systems, and cloud platforms.
Utilize cloud-based AI services on Azure or AWS, including platforms such as Azure Machine Learning and Amazon SageMaker.
Collaborate with business and technology stakeholders to translate business requirements into scalable AI solutions while tracking ROI and value realization.
Contribute to AI innovation and best practices through reusable frameworks, AI copilots, semantic models, knowledge graphs, LangChain/Semantic Kernel orchestration, and synthetic data techniques.