Associate Solutions Engineer - AI

SHI International Corp. • Piscataway, NJ

4d • $75,000 - $150,000

About The Position

Since 1989, SHI International Corp. has helped organizations change the world through technology. We’ve grown every year since, and today we’re proud to be a $16 billion global provider of IT solutions and services. Over 17,000 organizations worldwide rely on SHI’s concierge approach to help them solve what’s next. But the heartbeat of SHI is our employees – all 7,000 of them. If you join our team, you’ll enjoy: Our commitment to diversity, as the largest minority- and woman-owned enterprise in the U.S. Continuous professional growth and leadership opportunities. Health, wellness, and financial benefits to offer peace of mind to you and your family. World-class facilities and the technology you need to thrive – in our offices or yours. The Associate Solutions Engineer – AI is an early‑career technical role for engineers with strong foundations in machine learning, AI systems, and infrastructure who are ready to apply that knowledge in real‑world, customer‑facing enterprise environments. In this role, you will support the design, validation, and implementation of AI‑powered solutions across enterprise platforms, infrastructure, and data environments. Working alongside senior solution engineers, architects, and strategic partners, you will help translate advanced AI capabilities into scalable, production‑ready solutions for SHI customers. This role blends hands‑on development, AI systems knowledge, and solution delivery, with exposure to customer engagement and enterprise‑scale problem solving.

Requirements

• Core Technical Skills 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) Reinforcement learning–based optimization approaches (e.g., GRPO or similar) Solid foundation in software development and machine learning fundamentals, including: Model evaluation Performance analysis Systems & Infrastructure 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 Data & Tooling 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
• Presenting: Can prepare and deliver presentations, addressing key points and responding to questions with clarity.
• Negotiation: Can proactively seek out negotiation opportunities, initiate discussions, and contribute to conflict resolution.
• Communication: Can effectively communicate complex ideas and information, and can adapt communication style to the audience.
• Detail-Oriented: Can identify errors or inconsistencies in work and make necessary corrections.
• Organization: Can prioritize daily tasks, manage personal workflow, and utilize basic tools to keep track of responsibilities.
• Follow-Up: Can independently track and follow up on tasks without requiring reminders, ensuring responsibilities are fulfilled.
• Problem-Solving: Can identify problems, propose solutions, and take action to resolve them without explicit instructions.
• Relationship Building: Can identify opportunities for collaboration, propose strategies for effective communication, and build relationships without explicit instructions.
• Documentation: Can independently create and update documentation, ensuring accuracy and consistency, and can identify gaps or areas needing clarification.
• Results Orientation: Can set challenging goals for their team and lead them to achieve these goals, demonstrating a consistent track record of results.
• Ability to communicate complex technical concepts clearly to both technical and semi-technical audiences
• Interest in customer‑facing solution development and enterprise problem solving
• Willingness to collaborate with internal teams and external partners across the AI ecosystem
• Ability to balance learning, hands‑on engineering, and solution delivery in a fast‑paced environment

Nice To Haves

• 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

Responsibilities

• AI & Machine Learning Solution Development 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
• AI Infrastructure & Systems 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)
• Data, Pipelines & Tooling 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
• Customer & Partner Engagement 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

Benefits

• Health, wellness, and financial benefits to offer peace of mind to you and your family.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed