**Description** We are seeking a **hands-on AI & LLM 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. **Key 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. + 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. Target salary range: $120,001 - $160,000. The estimate displayed represents the typical salary range for this position based on experience and other factors. REQNUMBER: 2600092 SAIC is a premier technology integrator, solving our nation's most complex modernization and systems engineering challenges across the defense, space, federal civilian, and intelligence markets. Our robust portfolio of offerings includes high-end solutions in systems engineering and integration; enterprise IT, including cloud services; cyber; software; advanced analytics and simulation; and training. We are a team of 23,000 strong driven by mission, united purpose, and inspired by opportunity. Headquartered in Reston, Virginia, SAIC has annual revenues of approximately $6.5 billion. For more information, visit saic.com. For information on the benefits SAIC offers, see Working at SAIC. EOE AA M/F/Vet/Disability