AI/ML Engineer I

• Develop, integrate, and maintain AI/ML components within secure, production-grade applications
• Work directly with customers and internal engineering teams to troubleshoot system behavior and resolve defects
• Perform bug fixes, root cause analysis, and performance optimization across AI-enabled services
• Diagnosing and remediate data quality issues affecting model performance and system outputs
• Support data preprocessing, transformation, validation, and feature engineering pipelines
• Implement and optimize Large Language Model (LLM) integrations in secure cloud environments
• Assist in prompt engineering, response evaluation, and model tuning
• Support retrieval-augmented generation (RAG) pipelines and embedding workflows
• Participate in LLMOps practices including monitoring, logging, versioning, and performance tracking of deployed models
• Evaluate model outputs for consistency, hallucination risk, bias, and reliability
• Integrate AI services via REST APIs within backend systems
• Contribute to observability, telemetry, and audit logging aligned with compliance requirements
• Document technical implementations in accordance with secure development standards

Marginal Functions of the Job

• Other duties as assigned

Normal Work Schedule

This full-time role runs Monday to Friday, 8:30 AM-5:30 PM and requires flexibility to work remotely or on-site (if applicable per client RTO policies). On occasion additional hours may be necessary.

• Bachelor’s degree in Computer Science, Engineering, Data Science, or related technical field
• 1-3 years of professional experience in AI/ML engineering, data science, or software development
• Strong proficiency in Python
• Experience troubleshooting structured and unstructured data issues
• Familiarity with data preprocessing, feature engineering, and model validation techniques
• Hands-on experience working with OpenAI or similar LLM APIs
• Familiarity with foundational models and smaller language models (SLMs)
• Experience with transformer-based architecture (e.g., Hugging Face Transformers)
• Understanding of embeddings, tokenization, fine-tuning concepts, and inference workflows
• Experience in integrating AI services into cloud environments (Azure preferred)
• Understanding of LLMOps concepts including model monitoring, logging, prompt iteration, and deployment workflows
• Ability to debug model outputs and trace issues across data, prompts, and system integrations
• Eligible for U.S. Public Trust clearance.

Strongly Preferred Experience

• Experience implementing RAG architecture and vector-based search systems
• Familiarity with vector databases (e.g., FAISS, Pinecone, Azure AI Search)
• Exposure to containerization (Docker) and CI/CD practices
• Experience supporting AI systems in regulated or government environments
• Understanding of model evaluation metrics and performance benchmarking