Provide technical leadership and vision for large-scale distributed systems, ML platforms, and next-generation data infrastructure
Lead the design, architecture, and delivery of end-to-end solutions across the full SDLC, spanning development, testing, deployment, and operations
Drive innovation in Big Data, Generative AI, and Graph ML by translating emerging technologies into production-ready solutions
Architect scalable microservices and real-time inferencing systems leveraging modern ML infrastructure such as AI Agents, MCP, ML Inference, and LLM Gateway
Mentor and grow engineering teams, set technical direction, and foster a culture of engineering rigor, collaboration, and operational excellence
Champion best practices for building resilient, secure, and high-performance systems, optimized for scale and reliability

Degree in Computer Science, Mathematics, or a related field
10+ years of experience across the full software development lifecycle including design, coding, reviews, testing, deployment, and operations
5+ years of experience with distributed Big Data and ML platforms such as Spark, Lakehouse, Debezium, Kafka, Flink, or Hudi
Hands-on experience building Generative AI solutions such as RAG, AI Agents, and LLM fine-tuning in production
Experience working with Graph ML and Graph technologies such as GNNs
Strong track record of end-to-end solution ownership, from design through production scaling
5+ years of experience deploying large-scale solutions on cloud platforms such as AWS, Azure, or GCP
Proven ability to solve highly complex, ambiguous, cross-domain problems with measurable business impact.

AI Principal Machine Learning Engineer

Key skills