Build and maintain scalable batch and streaming data pipelines for ingestion, transformation, and curation
Design and optimize data models, feature stores, and storage patterns for AI/ML workloads
Implement Data Ops and MLOps automation (CI/CD, data validation, model deployment, monitoring, drift detection)
Design, build, and optimize agentic AI systems and LLM-powered applications (RAG pipelines, agent orchestration)
Develop and integrate AI services into secure, production-grade environments
Design and implement AI systems architecture best practices and standards across the organization
Build scalable, resilient cloud and on-premise systems for hosting AI/LLM applications
Provide infrastructure design, optimization, and monitoring support
Translate business requirements into technical solutions through cross-functional collaboration
Ensure code quality, system reliability, scalability, and observability

Expectations / Skillset Required:

Advanced Python proficiency with strong software engineering fundamentals (version control, testing, code reviews)
Proficiency in SQL and distributed computing (Spark, distributed systems)
Deep experience with data modeling and feature engineering across the ML lifecycle
Hands-on experience building LLM applications, RAG pipelines, and agentic AI frameworks (LangChain/LangGraph preferred)
Expertise designing distributed systems at enterprise scale
Strong CI/CD and DevOps knowledge (GitHub/GitHub Actions, Docker, Kubernetes)
Experience with event streaming platforms (Kafka)
Demonstrated ability to design and deploy hybrid cloud and on-premises systems (AWS, Azure)
Excellent collaboration and communication skills across engineering, research, and product teams
Self-directed problem-solving mindset and ability to navigate enterprise complexity
Master's degree in computer science, Software Engineering, or related field with 5\+ years in data/software/ML/AI engineering (preferred)
UI development expertise with React and JavaScript (a plus)

AI Engineer

Job Description