What are the responsibilities and job description for the ML Engineer (Generative AI) position at VASG (Vista Applied Solutions Group)?
Job Details
Job Title: ML Engineer (Generative AI with Fine-Tuning focus) Location: Irving TX - Onsite (3 Days Hybrid) - Need Texas Candidates only. Contract: C2C Experience: 11 Years
Key Responsibilities
- Work with our center of excellence for GenAI on creating groundbreaking solutions and conquering challenging projects.
- Build, fine-tune, and optimize locally hosted SLMs using curated golden questions and answers.
- Leverage expertise in models such as BERT, SBERT, and other transformer architectures to enhance language model performance.
- Design and execute fine-tuning workflows for both on-premises (NVIDIA A100 GPUs or similar) and cloud-based environments.
- Develop benchmarking frameworks to track model performance, quantify results, and establish measurable improvement metrics.
- Identify key parameters to evaluate and improve performance across various value driven use cases.
- Apply best practices in data refinement and preprocessing to ensure high-quality input for training and fine-tuning.
- Stay updated with the latest advancements in generative AI and machine learning technologies to incorporate innovative approaches.
- Collaborate with cross-functional teams, including data scientists, engineers, and non-technical stakeholders, to deliver effective AI solutions.
Qualifications
- Experience: 10 years in data science and machine learning.
Technical Expertise:
- Proven track record with transformer models (e.g., BERT, SBERT) and generative AI technologies.
- Experience with LLMs and SLMs, particularly in fine-tuning and deploying on-premises and cloud environments.
- Familiarity with GPU setups like NVIDIA A100s for model training and optimization.
Data Science Skills:
- Strong fundamentals in data refinement, preprocessing, and quality assurance.
- Proficient in designing benchmarking processes, tracking performance, and tying results to numerical metrics.
- Ability to identify and optimize key performance parameters for specific use cases.
- Communication Skills: Excellent verbal and written communication skills, with the ability to clearly articulate complex technical ideas to diverse audiences.
- Education: Bachelor's or Master s degree in Computer Science, Data Science, Machine Learning, or a related field.
Preferred Skills
- Experience with fine-tuning LLMs/SLMs in enterprise environments.
- 1-2 years of research-focused work for LLM / benchmarking.
- Familiarity with benchmarking tools and frameworks for performance evaluation.
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