AI ML Engineer / Solution Architect

Jobs via Dice; United States; Remote; USD 100,000 - 720,000; Full time

Job summary

A leading company in the tech industry is seeking an AI ML Engineer/Solution Architect for a long-term remote contract. The role involves implementing advanced fine-tuning techniques for large language models and optimizing performance for specific utility contexts. Candidates should have a strong background in deep learning and NLP, and be proficient with modern frameworks like PyTorch and TensorFlow. This position offers a unique opportunity to create customized AI solutions that deliver significant business value.

Qualifications

• Proficiency with advanced LLM training techniques.
• Experience with multi-GPU training and memory optimization.

Responsibilities

• Implement and optimize advanced fine-tuning approaches for models.
• Collaborate with MLOps engineer for model deployment and monitoring.

Skills

Deep Learning

NLP

Technical Adaptability

Tools

PyTorch

TensorFlow

Hugging Face Transformers

Job description

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Dice is the leading career destination for tech experts at every stage of their careers. Our client, SRS Consulting Inc, is seeking the following. Apply via Dice today!

Hello Associate,

Hope you are doing great,

Below positions is with Direct Client, Please share resumes

AI ML Engineer / Solution Architect

Long term Contract

Remote

Implementing GenAI requires specialized expertise in large language models. Traditional data scientists often haven't had the opportunity to dive deep into the practical intricacies of LLMs particularly advanced fine-tuning techniques, model compression strategies, memory optimization approaches, and specialized training workflows. This role requires a hands-on deep learning practitioner comfortable with modern frameworks and libraries specific to LLM development.

• Enables domain-specific fine-tuning of models to PG&E's unique utility context
• Improves model performance while reducing computational costs through advanced optimization techniques
• Creates PG&E-specific AI capabilities that address our unique operational challenges
• Enables the CoE to move beyond generic AI tools to customized solutions that deliver higher business value
  
  

Key Responsibilities:

• Implement and optimize advanced fine-tuning approaches (LoRA, PEFT, QLoRA) to adapt foundation models to PG&E's domain
• Develop systematic prompt engineering methodologies specific to utility operations, regulatory compliance, and technical documentation
• Create reusable prompt templates and libraries to standardize interactions across multiple LLM applications and use cases
• Implement prompt testing frameworks to quantitatively evaluate and iteratively improve prompt effectiveness
• Establish prompt versioning systems and governance to maintain consistency and quality across applications
• Apply model customization techniques like knowledge distillation, quantization, and pruning to reduce memory footprint and inference costs
• Tackle memory constraints using techniques such as sharded data parallelism, GPU offloading, or CPU+GPU hybrid approaches
• Build robust retrieval-augmented generation (RAG) pipelines with vector databases, embedding pipelines, and optimized chunking strategies
• Design advanced prompting strategies including chain-of-thought reasoning, conversation orchestration, and agent-based approaches
• Collaborate with the MLOps engineer to ensure models are efficiently deployed, monitored, and retrained as needed
  
  

Expected Skillset:

• Deep Learning & NLP: Proficiency with PyTorch/TensorFlow, Hugging Face Transformers, DSPy, and advanced LLM training techniques
• GPU/Hardware Knowledge: Experience with multi-GPU training, memory optimization, and parallelization strategies
• LLMOps: Familiarity with workflows for maintaining LLM-based applications in production and monitoring model performance
• Technical Adaptability: Ability to interpret research papers and implement emerging techniques (without necessarily requiring PhD-level mathematics)
• Domain Adaptation: Skills in creating data pipelines for fine-tuning models with utility-specific content
  
  

Thanks & Regards,

Rahul. B

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

• Industries

  Software Development

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