Senior AI Engineer (AI Foundations)
📍 Location: New York, NY, US
🏢 Industry: Financial Services
💼 Work Setting: Hybrid
Are you passionate about building cutting-edge AI platforms, developing large-scale machine learning solutions, and transforming business operations through advanced artificial intelligence? Join an innovative technology team where you will design, develop, and deploy enterprise AI solutions, optimize large language model performance, and contribute to the future of intelligent systems that drive meaningful business impact.
Key Responsibilities
AI Platform Engineering & Development
- Partner with cross-functional teams including software engineers, data scientists, researchers, product managers, and technical stakeholders to deliver AI-powered products and services.
- Design, develop, test, deploy, and maintain scalable AI and machine learning components across the full development lifecycle.
- Build and support solutions involving large language models, machine learning models, foundation model pipelines, intelligent search, model governance, monitoring, and evaluation frameworks.
- Develop production-grade AI systems that are reliable, secure, scalable, and cost-efficient.
Advanced AI & Machine Learning Solutions
- Create and optimize AI applications utilizing modern machine learning frameworks, open-source technologies, and cloud-native platforms.
- Develop capabilities such as semantic search, vector-based retrieval, model orchestration, guardrails, observability, experimentation, and model performance monitoring.
- Support the training, fine-tuning, inference, and deployment of machine learning and generative AI models.
- Implement solutions that improve model quality, operational efficiency, and business outcomes.
Performance Optimization & Scalability
- Research, design, and implement innovative approaches to improve AI system performance, latency, throughput, scalability, and infrastructure utilization.
- Optimize training and inference workloads to reduce operational costs while maintaining performance and reliability.
- Identify opportunities to enhance hardware and software efficiency across AI processing environments.
- Contribute to the development of best practices for enterprise-scale AI operations.
Innovation & Technical Leadership
- Stay current with emerging research, trends, and advancements in artificial intelligence, machine learning, and large language models.
- Evaluate new technologies, frameworks, and methodologies for potential adoption and business value.
- Translate academic research and innovative concepts into practical production solutions.
- Contribute to long-term AI strategy, architecture, and technology roadmaps.
Collaboration & Problem Solving
- Work closely with stakeholders to understand challenges and translate business needs into technical solutions.
- Analyze complex and ambiguous problems, identify root causes, and develop innovative approaches to solve them.
- Communicate technical concepts and recommendations effectively to both technical and non-technical audiences.
- Support continuous improvement through experimentation, learning, and knowledge sharing.
Required Qualifications
- Bachelor's degree in Computer Science, Artificial Intelligence, Computer Engineering, Electrical Engineering, Data Science, Mathematics, or a related technical discipline with relevant professional experience, or a Master's degree in a related field with applicable industry experience.
- Experience developing AI, machine learning, or advanced analytics solutions in production environments.
- Strong programming skills in Python, Java, Go, Scala, or similar modern programming languages.
- Strong foundation in software engineering, machine learning algorithms, and computer science fundamentals.
- Experience building scalable distributed applications and enterprise technology solutions.
Preferred Qualifications
- Experience deploying AI and machine learning solutions within cloud-based environments.
- Hands-on experience developing, delivering, and supporting AI-enabled products and services.
- Experience working with large language models, vector databases, semantic search, retrieval systems, and model governance frameworks.
- Knowledge of machine learning operations (MLOps), model monitoring, and production AI platforms.
- Experience optimizing model training and inference workloads to improve performance, latency, and cost efficiency.
- Familiarity with modern AI frameworks, open-source technologies, and cloud-native architectures.
Technical Skills
- Python, Java, Go, Scala, or equivalent programming languages.
- Machine learning and deep learning frameworks.
- Generative AI and large language model technologies.
- Vector databases, semantic search, and retrieval systems.
- Cloud computing and distributed systems.
- Model deployment, monitoring, observability, and governance.
- Performance tuning, scalability optimization, and infrastructure efficiency.
- Software engineering best practices and system design.