Sr AI Solution Engineer

About This Role

This role supports the full lifecycle of AI solutions, from ideation to productionization, for both Generative AI and Predictive AI use cases. The Senior Enterprise AI Solution Engineer implements AI-powered solutions, integrates them into enterprise workflows, and ensures operational reliability.

Responsibilities

• Lead major AI capabilities throughout the end-to-end AI solution lifecycle—from ideation and prototyping through deployment and operationalization
• Design and implement solutions leveraging Generative AI (LLMs, text generation, RAG pipelines) and Predictive AI (forecasting, anomaly detection, classification, computer vision)
• Translate business requirements into scalable technical capabilities, ensuring alignment with enterprise patterns and standards
• Develop APIs, microservices, and workflows to embed AI capabilities into production systems
• Work with data engineering teams to ensure robust data pipelines and features for AI models
• Optimize AI solutions for performance, cost, monitoring, and reliability in production environments
• Collaborate with business partners, engineering teams, and data scientists to iterate on high value use cases
• Communicate technical concepts to leadership and non technical stakeholders
• Participate in AI solution architecture design and contribute to cross functional architecture decisions
• Establish and follow best practices for model deployment, versioning, testing, and observability
• Contribute to ML Ops pipelines, CI/CD automation, and containerized deployments (Kubernetes, Docker)
• Perform production support and troubleshooting for deployed AI services
• Mentor junior AI engineers and contribute to team knowledge sharing
• Support AI solution engineering leaders in establishing engineering standards and reusable patterns

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field
• 6-8 years of experience in AI/ML engineering, data science, or software development
• Proficiency in Python and modern ML frameworks (TensorFlow, PyTorch)
• Hands-on experience with cloud platforms (Azure, AWS, GCP) and containerization (Docker, Kubernetes)
• Experience building LLM based systems, RAG pipelines, or generative AI applications
• Understanding of data pipelines, APIs, microservices, and distributed systems
• Experience with ML Ops tools and practices (model registries, CI/CD, observability)
• Strong problem solving skills with the ability to troubleshoot complex systems
• Excellent written and verbal communication skills

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field
• 6-8 years of experience in AI/ML engineering, data science, or software development

Nice to Have

• Curiosity and commitment to staying current with emerging AI technologies

Skills

* Required skills

Benefits