Associate Solutions Engineer - AI at SHI… · Piscataway

Summary: SHI International Corp. is a global provider of IT solutions and services, and they are seeking an Associate Solutions Engineer – AI. This early-career technical role involves supporting the design, validation, and implementation of AI-powered solutions across enterprise platforms and data environments, while collaborating with senior engineers and engaging with customers. Responsibilities: - Support the design and implementation of AI and generative AI solutions using modern ML frameworks and enterprise platforms - Assist in translating advanced AI concepts (e.g., fine‑tuning, LoRA, reinforcement learning–based optimization, perception systems) into deployable enterprise architectures - Contribute to solution prototypes, proofs of concept (POCs), and lab‑based demonstrations for customers - Work with AI infrastructure stacks, including accelerators, kernels, training pipelines, and performance optimization - Assist in evaluating and optimizing AI workloads across hardware platforms (GPUs, AI accelerators, optimized kernels) - Support AI deployment patterns such as model training, inference, and retrieval‑augmented generation (RAG) - Build and support AI‑related pipelines for: Data ingestion and preprocessing, Model evaluation and benchmarking, Failure analysis, logging, and observability - Develop internal and customer‑facing tools or dashboards to visualize performance, system behavior, or AI outputs - Participate in technical workshops, solution briefings, and architecture sessions with customers - Help explain AI system behavior, limitations, and performance trade‑offs to technical and semi‑technical audiences - Collaborate with cloud, silicon, and ISV partners across the AI ecosystem - Prepare and deliver presentations, addressing key points and responding to questions with clarity - Proactively seek out negotiation opportunities, initiate discussions, and contribute to conflict resolution - Effectively communicate complex ideas and information, adapting communication style to the audience - Identify errors or inconsistencies in work and make necessary corrections - Prioritize daily tasks, manage personal workflow, and utilize basic tools to keep track of responsibilities - Independently track and follow up on tasks without requiring reminders, ensuring responsibilities are fulfilled - Identify problems, propose solutions, and take action to resolve them without explicit instructions - Identify opportunities for collaboration, propose strategies for effective communication, and build relationships without explicit instructions - Independently create and update documentation, ensuring accuracy and consistency, and identify gaps or areas needing clarification - Set challenging goals for their team and lead them to achieve these goals, demonstrating a consistent track record of results Required Qualifications: - Strong foundations in machine learning, AI systems, and infrastructure - Support the design and implementation of AI and generative AI solutions using modern ML frameworks and enterprise platforms - Assist in translating advanced AI concepts (e.g., fine‑tuning, LoRA, reinforcement learning–based optimization, perception systems) into deployable enterprise architectures - Contribute to solution prototypes, proofs of concept (POCs), and lab‑based demonstrations for customers - Work with AI infrastructure stacks, including accelerators, kernels, training pipelines, and performance optimization - Assist in evaluating and optimizing AI workloads across hardware platforms (GPUs, AI accelerators, optimized kernels) - Support AI deployment patterns such as model training, inference, and retrieval‑augmented generation (RAG) - Build and support AI‑related pipelines for data ingestion and preprocessing, model evaluation and benchmarking, failure analysis, logging, and observability - Develop internal and customer‑facing tools or dashboards to visualize performance, system behavior, or AI outputs - Participate in technical workshops, solution briefings, and architecture sessions with customers - Help explain AI system behavior, limitations, and performance trade‑offs to technical and semi‑technical audiences - Collaborate with cloud, silicon, and ISV partners across the AI ecosystem - Can prepare and deliver presentations, addressing key points and responding to questions with clarity - Can proactively seek out negotiation opportunities, initiate discussions, and contribute to conflict resolution - Can effectively communicate complex ideas and information, and can adapt communication style to the audience - Can identify errors or inconsistencies in work and make necessary corrections - Can prioritize daily tasks, manage personal workflow, and utilize basic tools to keep track of responsibilities - Can independently track and follow up on tasks without requiring reminders, ensuring responsibilities are fulfilled - Can identify problems, propose solutions, and take action to resolve them without explicit instructions - Can identify opportunities for collaboration, propose strategies for effective communication, and build relationships without explicit instructions - Can independently create and update documentation, ensuring accuracy and consistency, and can identify gaps or areas needing clarification - Can set challenging goals for their team and lead them to achieve these goals, demonstrating a consistent track record of results - Strong proficiency in Python for machine learning, systems tooling, and data workflows - Experience with PyTorch and modern ML training and inference workflows - Understanding of fine‑tuning and optimization techniques, including LoRA (Low‑Rank Adaptation) and reinforcement learning–based optimization approaches (e.g., GRPO or similar) - Solid foundation in software development and machine learning fundamentals, including model evaluation and performance analysis - Exposure to AI accelerators, kernels, or low‑level optimization concepts - Familiarity with ML infrastructure pipelines beyond model‑level code - Experience with profiling, debugging, and performance tuning of ML workloads - Basic exposure to AI platforms and infrastructure including GPUs, networking, storage, and data‑center technologies - Experience building data pipelines for logs, metrics, or ML inputs - Comfort working with both structured and unstructured data - Experience working across different data sources and formats Preferred Qualifications: - Experience with AI solution domains such as generative AI, agentic AI systems, computer vision or perception systems, robotics - Experience benchmarking, comparing, or evaluating machine learning models - Exposure to low‑level or systems‑level optimization (e.g., kernel‑level tuning) - Familiarity with AI frameworks or SDKs such as CUDA, XLA, TensorRT, Neuron, NKI - Exposure to NVIDIA platforms and frameworks (e.g., NeMo, NIMs) - Understanding of modern AI workflows including graph databases, vector databases, guardrails and inference pipelines - Experience working with ML platforms across cloud, hybrid, or on‑prem environments - Familiarity with containerization and deployment tools (e.g., Docker) - Experience developing visualization or dashboard tools (e.g., React, Node.js, or similar frameworks) - Research or applied experience translating academic AI concepts into production‑ready systems Required Skills: Python, PyTorch, Machine Learning Fundamentals, Model Evaluation, Performance Analysis, Fine-Tuning Techniques, LoRA (Low-Rank Adaptation), Reinforcement Learning Optimization, AI Accelerators, Kernel-Level Optimization, ML Infrastructure Pipelines, Profiling, Debugging ML Workloads, AI Platforms, Infrastructure, Data Pipelines, Structured, Unstructured Data Handling, Generative AI, Agentic AI Systems, Computer Vision, Robotics, AI Frameworks, SDKs, CUDA, XLA, TensorRT, Neuron SDK, NKI SDK, NVIDIA Platforms, Graph Databases, Vector Databases, Inference Pipelines, Cloud ML Platforms, Hybrid ML Platforms, On-Prem ML Platforms, Containerization, Docker, Visualization Tools, React, Node.js, Follow-Up, Results Orientation Benefits: Medical, Vision, Dental, 401K, Flexible spending