Senior Artificial Intelligence Engineer

• This role is ideal for someone passionate about enabling AI innovation through robust infrastructure, intuitive tooling, and seamless integration of cutting-edge models.
• You’ll be at the forefront of operationalizing AI—designing systems that empower teams to build, deploy, and iterate on intelligent applications with speed and reliability
• Platform Architecture & Development: Design and implement scalable AI platforms using Python. Integrate MLOps tools for model versioning, deployment, monitoring, and lifecycle management
• AI Tooling & LLM Capabilities: Build tools and abstractions to interface with large language models, including prompting frameworks, agentic workflows (e.g., with LangGraph), and LLM orchestration services
• Data Engineering & ETL: Collaborate with data teams to build robust ETL pipelines, preprocess training data, and construct feature workflows that feed AI models at scale
• Reliability & Monitoring: Implement model monitoring dashboards to ensure platform reliability and performance, and investigate production prompt results
• Research & Innovation: Stay ahead of cutting-edge trends in Generative AI. Prototype AI scenarios that unlock new product or operational value
• Testing, Documentation & Standards: Define rigorous unit, integration, and performance testing methodologies. Maintain comprehensive documentation and enforce best practices for ethical and secure AI usage

Benefits

• Premium Health Benefits
• Distributed Team Building
• Learning & Development
• Award Winning Culture- Solid understanding of MLOps principles: CI/CD, model versioning, monitoring, metrics
• Familiarity with LLM prompting design, agentic workflows (e.g. LangGraph)
• Nice to Have
• Exceptional communication and collaboration across engineering and product teams
• Bachelor’s or Master’s in Computer Science, Engineering, or related field
• Awareness of AI ethics, biases, fairness, and data privacy protocols
• 4+ years of experience building production-grade AI/ML platforms or developer-centric AI tools
• Experience with large-scale data pipelines (e.g., Apache Spark, Kafka)
• Certifications in cloud platforms, ML engineering, or specific AI tooling
• Strong software engineering skills in Python (preferred) and familiarity with Java/Go and OOP/data structures
• Experience building and deploying on cloud platforms (AWS, Google Cloud, Azure); familiar with Docker and Kubernetes