AI Platform Engineer, Training and Inference

• Experience in ML engineering with time in an ML platform or MLOps role
• Production Ray depth: Ray Train, Serve, Core, and Data — debugged real production failures including NCCL timeouts, Plasma OOM, and Serve autoscaling lag
• LLM serving engines: hands-on with vLLM, SGLang, or NVIDIA Triton — PagedAttention, prefix caching, and continuous batching tuned for latency/throughput targets
• Distributed training: DDP, FSDP, NCCL collectives, gradient checkpointing, and mixed precision (BF16/FP8)
• RL working knowledge: PPO, policy gradient, or RLHF — able to translate an algorithm into distributed compute primitives

• Model lifecycle operations: MLflow registry, shadow/A/B/canary patterns, and auto-
rollback on golden signal degradation

• Vector databases: Pgvector or Qdrant — ANN index strategies, embedding upsert, and query latency tuning under inference load
• Strong Python and PyTorch; Flyte or equivalent ML orchestrator
• Quantization (nice to have): INT8/INT4/FP8 post-training quantization (GPTQ, AWQ, or bitsandbytes)
• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent
practical experience or equivalent military experience

We offer you a competitive total rewards package, learning and tremendous opportunities to grow and advance in your career. At Saviynt, it is not typical for an individual to be hired at or near the top of the range for their role and final compensation decisions are dependent on many factors including, but not limited to location; skill sets; experience and training; licensure and certifications; and other relevant business and organizational needs.

You may also be eligible to participate in a Saviynt discretionary bonus plan, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.