AI Engineer

We are looking for a motivated Junior AI Engineer to join our team. In this hybrid technical + presales role, you will work on AI/ML model development while supporting the commercialization of our GPU server and AI solutions. You will collaborate closely with engineering, product, and sales teams to ensure technical accuracy and readiness of product materials, documentation, and pricing.

Key Responsibilities

AI Engineering Responsibilities

Model Development: Assist inbuilding, training, and evaluating machine learning and deep learning models.
Data Preparation: Collect, clean, and preprocess structured and unstructured datasets.
Experimentation & Benchmarking: Run experiments, prototype solutions, and compare model performance.
Deployment Support: Assist in deploying AI models into production environments (APIs, cloud, edge).
Monitoring & Optimization: Support model performance monitoring and implement improvements.
Documentation: Maintain documentation for datasets, experiments, and internal tools.

Presales & Product Enablement Responsibilities

GPU Server Product Manuals: Support the creation and maintenance of technical documentation, including hardware/software specifications, setup guides, and configuration recommendations.
White Papers & Technical Collateral: Contribute to white papers, solution briefs, and technical reference materials to help customers understand AI infrastructure and workloads.
Pricing Readiness: Assist the business and presales team in preparing pricing models, cost estimations, and competitive comparisons for GPU servers and AI solutions.
Product Evaluation & Testing: Participate in benchmarking GPU hardware, validating performance claims, and testing new AI accelerators or configurations.
Customer Technical Support (Presales): Provide technical input during client discussions, including product demos, PoCs, and solution walkthroughs.
Cross‑functional Collaboration: Work with sales, marketing, and product management to ensure technical consistency across all external materials.

Required Skills

• Solid knowledge of Python and ML libraries (NumPy, Pandas, scikit‑learn).
• Basic familiarity with deep learning frameworks (PyTorch, TensorFlow).
• Understanding of machine learning fundamentals and evaluation metrics.
• Knowledge of Linux environments and basic GPU computing concepts (CUDA is a bonus).
• Strong written communication skills for documentation and presales materials.
• Experience with Git and standard engineering workflows.

Nice-to-Have Skills

• Experience with LLMs, NLP, or vectordatabases.
• Familiarity with GPU architectures, benchmarking tools, or AI hardware.
• Exposure to ML Ops, Docker, Kubernetes, or cloud platforms (AWS/Azure/GCP).
• Basic understanding of pricing structures for compute or cloud resources.
• Prior experience in presales, technical marketing, or solution engineering.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, AI/ML, or related fields.
• 0–2 years of experience in AI/ML or technical engineering roles.
• Academic, internship, or personal projects involving ML/AI are a plus.