Platform Engineer II
Confirmed on the employer's own hiring board on Oct 24, 2026. First seen by Alion on Oct 21, 2026.
Job Summary
The Generative AI Engineer will be responsible for designing, developing, and deploying AI-driven solutions leveraging large language models (LLMs), diffusion models, and other generative frameworks. The role involves collaborating closely with data scientists, software engineers, and business teams to create intelligent, context-aware applications that enhance automation, creativity, and decision-making across the enterprise.
Key Responsibilities
Design, fine-tune, and deploy large language models (LLMs) and other generative models for specific business use cases.
- Build and optimize prompt engineering and model interaction strategies for improved output accuracy and relevance.
- Develop APIs, pipelines, and integrations to embed generative AI capabilities into products and workflows.
- Collaborate with data engineers to ensure high-quality, domain-relevant datasets for model training and evaluation.
- Implement responsible AI principles, including bias mitigation, explainability, and data governance.
- Monitor model performance and continuously refine models through feedback loops and retraining.
- Stay up to date with emerging trends in AI research, frameworks (e.g., LangChain, Hugging Face, OpenAI API), and open-source tools.
- Contribute to internal knowledge sharing and best practices for scalable AI deployment.
Skill Requirements
1. Solid proficiency in STD cell characterization techniques and tools.
2. Strong understanding of semiconductor device physics and measurement systems.
3. In-depth knowledge of data analysis software used in characterization workflows.
4. Ability to apply advanced characterization standards and methodologies to project tasks.
5. Solid communication skills for intra-team collaboration and technical reporting.
Other Requirements
o Proficiency in Python and frameworks such as PyTorch or TensorFlow.
o Experience in SQL, Machine Learning, Big Dat and other Databases.
o Experience with LLM APIs (OpenAI, Anthropic, Gemini, etc.) and vector databases (Pinecone, FAISS, Weaviate).
o Familiarity with prompt engineering, model fine-tuning, and reinforcement learning from human feedback (RLHF).
o Strong understanding of NLP, deep learning, and data pipeline design.
o Exposure to cloud platforms (AWS, Azure, GCP) and MLOps tools for model deployment.
o Strong problem-solving and analytical thinking.