AI Scientist (Full Time job and Fully Remote) USA

AI Scientist

Fulltime

Remote

AI Scientist: ML Engineering, Model Evaluation, Feedback Loops, Text2SQL, LLM Integrations, Embedding/Chunking Strategies, Prompt Engineering, Experimentation with Model Selection, Model Finetuning

Key Responsibilities

Model Fine-Tuning & Optimization:

o Customize and fine-tune large generative models (e.g., GPT, LLaMA, BERT, or

Anthropic) for specific applications and domains.

o Develop efficient fine-tuning pipelines leveraging AWS services such as SageMaker, S3,

Bedrock, etc.

o Experiment with advanced techniques, including parameter-efficient fine-tuning (e.g.,

LoRA, adapters) and multi-task learning.

Evaluation & Validation:

o Design rigorous evaluation frameworks for generative models, including metrics for

quality, bias, robustness, and explainability.

o Benchmark model performance on domain-specific datasets using tools like Hugging

Face Evaluate, AWS CloudWatch, and custom evaluation pipelines.

o Conduct A/B testing to validate improvements and ensure alignment with business

objectives.

Text-to-SQL & Generative AI-driven Solutions:

o Develop GenAI-driven Text-to-SQL solutions to automate database queries based on

natural language input.

o Optimize GenAI workflows for database interactions and information retrieval.

Embedding/Chunking & Prompt Engineering:

o Design and implement embedding and chunking strategies for scalable data

processing.

o Utilize prompt engineering techniques to fine-tune the performance of AI models in

production environments.

Generative AI Research:

o Investigate and propose enhancements to generative model architectures and training

paradigms.

o Explore novel use cases of generative AI in areas like NLP, computer vision, and

multimodal tasks.

Required Skills and Qualifications

Educational Background:

o Ph.D. or Master's degree in Machine Learning, Computer Science, Artificial Intelligence,

or related field.

o Specialization or strong focus on generative models, deep learning, or NLP.

GenAI Expertise:

o Proven experience in fine-tuning and evaluating large language or vision-based

generative models (e.g., GPT, T5, Stable Diffusion).

o Familiarity with libraries and frameworks like Hugging Face Transformers, TensorFlow,

or PyTorch.

o Knowledge of generative techniques such as diffusion models, transformers, GANs, and

autoregressive architectures.

AWS Technology Stack:

o Hands-on experience with AWS SageMaker for training and deploying generative

models.

o Proficiency in AWS storage, compute, and AI services (e.g., S3, EFS, EC2, Lambda,

SageMaker JumpStart).

o Experience with MLOps tools on AWS (e.g., SageMaker Pipelines, CloudFormation,

CodePipeline).

Evaluation & Metrics:

o Expertise in designing evaluation protocols for generative models, including BLEU,

ROUGE, perplexity, and human-in-the-loop feedback systems.

o Familiarity with explainability tools (e.g., SHAP, LIME) and ethical AI evaluation

frameworks.

Programming Skills:

o Strong proficiency in Python and familiarity with frameworks for distributed training (e.g.,

Dask, Ray, or PyTorch Distributed).

o Experience developing scalable ML workflows using AWS Step Functions and

EventBridge.

Thanks and Regards,

Rajiv Kumar

Associate Manager

TestingXperts Next Gen Specialist QA & Software Testing Company

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