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

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

• 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

Other Requirements

• 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

What This Role Is Not

• Not a pure machine learning research role

• Not a pure software engineering role isolated from customers

• Not a presales‑only role without hands‑on technical work

This role sits between solution development, engineering, and applied delivery, and is designed to grow technically strong engineers into trusted enterprise AI solution leaders.