Minimum Education and/or Training

• Master’s degree in computer science, computer engineering, data science, informationtechnologyor related fieldrequired
• PhD degree in data science, computer science, computer engineering, informationtechnologyor related field preferred.

Minimum Experience

• 4 years in designing and developing solutions for large scale AI/Machine Learning (ML) systems and/or building solutions for a product on AI/ML features and capabilities.
• Strong background in industry use cases built on deep learning and machine learning (unsupervised and supervised techniques) isa must
• Deep learning frameworks such as TensorFlow,Keras,PyTorch, Time series analysis, anomaly detection, forecasting, predictive modeling, graph- basedneural networks, Bayesian statistics, and text analytics area must

Preferred Qualifications

• Hands-on experience training, fine-tuning, or adapting LLMs or multi-modal foundation models (e.g., PEFT/LoRA, instruction tuning, preference optimization), including debugging failure modes such as catastrophic forgetting or training instability.
• Experience diagnosing and resolving distributed/multi-GPU training or inference issues (e.g., NCCL communication hangs, CUDA out-of-memory errors, load-balancing across nodes) and scheduling AI workloads on HPC (e.g.,Slurm) to maximize GPUutilization
• Experience designing safety guardrails and sandboxing for agentic systems: tool-access scoping, prompt-injection defense,secretsmanagement, audit logging, and containment of failures.
• Experienceoptimizinginference cost, latency, and resource usage (e.g., KV-cache management, quantization, batching, speculative decoding, high-throughput serving viavLLMTensorRT-LLM/Triton) and judgment about when a simpler deterministic pipeline is a better fit than an agentic one.
• Experience safeguarding data security and privacy for AI systems handling sensitive research data, including access controls and institutional data-use/security policies.
• Experience building rigorous, reproducible benchmarking/evaluation frameworks that separate genuine model improvement from prompt overfitting, retrieval effects, or evaluator bias.
• Experience building and scaling AI/ML pipelines and workflows on HPC or cloud environments.
• Demonstrated ownership of a system beyond the prototype stage (observability, versioning, rollback, cost control, incident response).
• Contributions to open-source AI/ML infrastructure projects (e.g.,vLLM,PyTorch, Ray, Hugging Face) or a publictrack record(GitHub, Hugging Face, papers) are a plus.
• Familiarity with biomedical research, imaging, or regulated health data is a plus.
• Demonstrated technical leadership: setting standards, mentoring, and cross-team collaboration.