Saic
Overview
SAIC is seeking a hands-on
AI Engineer
to join the AWS AI/GenAI Solutions Team within the IRS Advanced Analytics Program (AAP). This role focuses on the practical development and deployment of large language models (LLMs) and GenAI solutions using AWS services such as SageMaker, Bedrock, and open-source frameworks. The engineer will be directly responsible for coding pipelines, fine-tuning models, building inference endpoints, and integrating GenAI workflows into production. By working closely with data engineers, architects, and Trustworthy AI specialists, this role ensures that GenAI capabilities are secure, scalable, and aligned with IRS mission needs. Salary : Target salary range: $120,001 - $160,000. The estimate displayed represents the typical salary range for this position based on experience and other factors. Location : REMOTE WORK, TX, United States Category : Engineering and Sciences •
Subcategory : Solutions Archt •
Schedule : Full-time •
Shift : Day Job •
Travel : No Minimum Clearance Required : None •
Clearance Level Must Be Able to Obtain : Public Trust •
Potential for Remote Work : Remote Responsibilities
Build
end-to-end LLM pipelines : data preparation, training, fine-tuning, and evaluation of models using SageMaker and Bedrock. Develop
prompt engineering strategies, chaining pipelines, and custom evaluation scripts
to validate LLM behavior. Implement
RAG (retrieval-augmented generation) workflows
by integrating LLMs with IRS data sources. Code and deploy
inference endpoints, APIs, and integration layers
for mission teams to consume LLM services. Optimize
model performance, latency, and cost
through benchmarking, hyperparameter tuning, and scaling strategies. Embed
bias detection, fairness, and explainability checks
in model pipelines, in partnership with Trustworthy AI specialists. Contribute to
CI/CD automation
for LLM deployments, including rollback and retraining workflows. Write
production-grade Python code
and leverage frameworks such as Hugging Face Transformers, LangChain, PyTorch, or TensorFlow. Document workflows and create
reusable templates/accelerators
for faster onboarding of new GenAI use cases. Participate in
hands-on troubleshooting and debugging
of pipelines, deployments, and model behavior. Qualifications
Required Qualifications Bachelor’s or master’s degree in computer science, Data Science, or related field. Ability to obtain and maintain a Public Trust requiring U.S. Citizenship 5+ years of
hands-on AI/ML engineering experience , including direct model training, fine-tuning, and deployment. Strong expertise in
Python programming
and ML/LLM frameworks (Hugging Face, LangChain, PyTorch, TensorFlow). Experience with
AWS AI services
(SageMaker, Bedrock, S3, Lambda, Step Functions) in production workflows. Proven ability to build and deploy
inference endpoints and APIs
for AI/ML workloads. Familiarity with
CI/CD pipelines
and IaC (Terraform, CloudFormation) for model deployment. Practical understanding of
LLM evaluation methods
(prompt testing, bias/toxicity detection, response consistency). Desired Skills Certifications:
AWS Certified Machine Learning Specialty
or equivalent. Experience implementing
RAG pipelines
or multi-model orchestration for enterprise use cases. Familiarity with
federal compliance frameworks
(FedRAMP, NIST 800-53) and secure AI/ML operations. Knowledge of
Trustworthy AI principles
(auditability, explainability, fairness) in LLM contexts. Strong problem-solving skills and ability to
debug real-world AI/LLM issues
in production. Notes
Overview continued: SAIC accepts applications on an ongoing basis and there is no deadline. SAIC is an Equal Opportunity Employer. For more information, visit saic.com. For ongoing news, please visit our newsroom. Are you an SAIC Employee?
Please apply through the internal career site here >
#J-18808-Ljbffr
SAIC is seeking a hands-on
AI Engineer
to join the AWS AI/GenAI Solutions Team within the IRS Advanced Analytics Program (AAP). This role focuses on the practical development and deployment of large language models (LLMs) and GenAI solutions using AWS services such as SageMaker, Bedrock, and open-source frameworks. The engineer will be directly responsible for coding pipelines, fine-tuning models, building inference endpoints, and integrating GenAI workflows into production. By working closely with data engineers, architects, and Trustworthy AI specialists, this role ensures that GenAI capabilities are secure, scalable, and aligned with IRS mission needs. Salary : Target salary range: $120,001 - $160,000. The estimate displayed represents the typical salary range for this position based on experience and other factors. Location : REMOTE WORK, TX, United States Category : Engineering and Sciences •
Subcategory : Solutions Archt •
Schedule : Full-time •
Shift : Day Job •
Travel : No Minimum Clearance Required : None •
Clearance Level Must Be Able to Obtain : Public Trust •
Potential for Remote Work : Remote Responsibilities
Build
end-to-end LLM pipelines : data preparation, training, fine-tuning, and evaluation of models using SageMaker and Bedrock. Develop
prompt engineering strategies, chaining pipelines, and custom evaluation scripts
to validate LLM behavior. Implement
RAG (retrieval-augmented generation) workflows
by integrating LLMs with IRS data sources. Code and deploy
inference endpoints, APIs, and integration layers
for mission teams to consume LLM services. Optimize
model performance, latency, and cost
through benchmarking, hyperparameter tuning, and scaling strategies. Embed
bias detection, fairness, and explainability checks
in model pipelines, in partnership with Trustworthy AI specialists. Contribute to
CI/CD automation
for LLM deployments, including rollback and retraining workflows. Write
production-grade Python code
and leverage frameworks such as Hugging Face Transformers, LangChain, PyTorch, or TensorFlow. Document workflows and create
reusable templates/accelerators
for faster onboarding of new GenAI use cases. Participate in
hands-on troubleshooting and debugging
of pipelines, deployments, and model behavior. Qualifications
Required Qualifications Bachelor’s or master’s degree in computer science, Data Science, or related field. Ability to obtain and maintain a Public Trust requiring U.S. Citizenship 5+ years of
hands-on AI/ML engineering experience , including direct model training, fine-tuning, and deployment. Strong expertise in
Python programming
and ML/LLM frameworks (Hugging Face, LangChain, PyTorch, TensorFlow). Experience with
AWS AI services
(SageMaker, Bedrock, S3, Lambda, Step Functions) in production workflows. Proven ability to build and deploy
inference endpoints and APIs
for AI/ML workloads. Familiarity with
CI/CD pipelines
and IaC (Terraform, CloudFormation) for model deployment. Practical understanding of
LLM evaluation methods
(prompt testing, bias/toxicity detection, response consistency). Desired Skills Certifications:
AWS Certified Machine Learning Specialty
or equivalent. Experience implementing
RAG pipelines
or multi-model orchestration for enterprise use cases. Familiarity with
federal compliance frameworks
(FedRAMP, NIST 800-53) and secure AI/ML operations. Knowledge of
Trustworthy AI principles
(auditability, explainability, fairness) in LLM contexts. Strong problem-solving skills and ability to
debug real-world AI/LLM issues
in production. Notes
Overview continued: SAIC accepts applications on an ongoing basis and there is no deadline. SAIC is an Equal Opportunity Employer. For more information, visit saic.com. For ongoing news, please visit our newsroom. Are you an SAIC Employee?
Please apply through the internal career site here >
#J-18808-Ljbffr