GenAI/LLM Engineer

Portland - USA

Monthly Salary: Not Disclosed

Posted on: 30+ days ago

Vacancies: 1 Vacancy

Job Summary

Job Title: GenAI/ LLM Engineer

Location: Portland OR (Remote)

Duration: Long Term (Contract)

Interview mode: Virtual

Visa: USC/ GC Only

Job Description-

Must have Valid LinkedIn profile.

Relocation Offered: Yes (try to find PST/CST profiles)

Key Responsibilities:

Implement and optimize advanced fine-tuning approaches (LoRA PEFT QLoRA) to adapt foundation models to PG&Es 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 CPUGPU 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

Job Title: GenAI/ LLM Engineer Location: Portland OR (Remote) Duration: Long Term (Contract) Interview mode: Virtual Visa: USC/ GC Only Job Description- Must have Valid LinkedIn profile. Relocation Offered: Yes (try to find PST/CST profiles) Key Responsibilities: Implement and optimize advan...

Job Title: GenAI/ LLM Engineer

Location: Portland OR (Remote)

Duration: Long Term (Contract)

Interview mode: Virtual

Visa: USC/ GC Only

Job Description-

Must have Valid LinkedIn profile.

Relocation Offered: Yes (try to find PST/CST profiles)

Key Responsibilities:

Implement and optimize advanced fine-tuning approaches (LoRA PEFT QLoRA) to adapt foundation models to PG&Es 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 CPUGPU 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

Key Skills

ASP.NET
Health Education
Fashion Designing
Fiber
Investigation

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About Company

JS Consulting

MMJS is a consulting firm that is focused on adding value to its clients business through Tax, Technology and Management Consulting. Strategically located in Bahrain, Oman, UAE, Kuwait and KSA to support clients across GCC.. Having served 300+ clients, MMJS has acquired the trust of m ... View more

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