ML Engineer-Software engineering + distributed systems + LLMOps

Title : ML Engineer
Location : Palo Alto, CA (Onsite)

F2F round of Interview

Job Description:

• Strong working experience with big data tools: Spark, Kafka, Snowflake, etc
• Experience in AWS/Azure cloud architectures
• Partitioning recommendation for data in memory
• Managing a multi-user environment
• Experienced with common SQL & NO SQL databases, e.g. PostgreSQL, MongoDB, Redis
• Experience developing applications in Python/Java
• Strong knowledge in object-oriented/object function scripting languages: Python, Java, Scala, etc
• Hands-on experience working with Web Services and RESTful APIs
• Experienced with production deployment using CI/CD
• Strong working experience in Linux/Unix environment
• Understanding of system design, data structures, and algorithms
• Understanding of algorithmic design and complexity analysis
• Experience with design patterns and architecture design.
• Experience with high-performance and highly scalable distributed systems and multi-threaded application development.
• Experience developing large-scale applications
• Understanding of standard design pattern
• Experience in Early stages of Software Development and testing

GenAI skill knowledge:

• Experience productionizing AI/ML or LLM-powered workflows (using LangGraph, LangChain, CrewAI etc.) with a focus on reliability, reproducibility, and auditability
• Strong understanding of LLMOps fundamentals, including prompt/model/config versioning and traceable run metadata.
• Ability to build and maintain an LLM evaluation framework (golden datasets, regression tests, scoring/rubrics, quality thresholds, trend tracking) for non-deterministic outputs
• Proven ability to implement observability for LLM pipelines, including structured logging, metrics, dashboards, alerting, latency breakdown, error taxonomy, and token/cost tracking
• Experience designing and integrating tool-calling / agent skills (function calling, tool interfaces, input/output schemas, guardrails, structured outputs) into data pipelines or services
• Experience with API reliability patterns relevant to model calls (rate limiting, retries/backoff, circuit breakers, timeouts, idempotency, replay/backfill)
• Demonstrated curiosity and self-learning ability in fast-evolving GenAI industry; able to iterate quickly and then harden solutions for production