Senior Full Stack AI Engineer

Job Title: Full stack AI Engineer Work Location & Reporting Address New York, NY/Pittsburgh, PA/Lake Mary, FL ( Hybrid (2-4 days a week work from office)

Contract duration: 12 months

Target Start Date: 14 Sep 2026

Does this position require Visa independent candidates only? Yes

Interview Process (Is face to face required?) Video mode

Job Details Minimum years of experience required 5+ years of relevant experience; 10+ years of overall experience

Certification needed: No

Must Have Skills: AI, Python

Nice to Have Skills Java Spring boot, Angular

Detailed Job Description

• 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.
• Experience of working for banking domain
• 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