• Design and build simulation environments that model CTV auction mechanics, inventory supply, and advertiser competition • Develop counterfactual and what-if frameworks for evaluating bidding strategies, budget allocation, and pacing algorithms offline • Build AI agents that explore strategy spaces, generate hypotheses, and automate experimentation within simulated environments • Use LLMs and generative AI to accelerate internal ML workflows — synthetic data generation, code generation, automated analysis, and rapid prototyping • Use simulation to de-risk ML model deployments — validate new bidding and optimization strategies before they touch live traffic • Define the technical direction for simulation and AI infrastructure and mentor engineers on the team

• Write production Python that powers real-time bidding, model training, and campaign optimization • Train, deploy, and monitor ML models that decide which ads to show, when, and at what price: millions of bid decisions per second • Build and improve our incrementality measurement systems: helping advertisers understand the true causal lift of their CTV spend • Design and implement new ML products across the ad-buying lifecycle: audience targeting, bid optimization, pacing, and attribution • Use LLMs and generative AI to build internal tools that accelerate how we develop, test, and ship ML systems • Serve as a technical lead and mentor on a distributed engineering team

• Scale the decisionmaking process for tools for the tvScientific AI team, from our workflows to our training infrastructure to our Kubernetes deployments • Improve the developer experience for the data science team • Upgrade our observability tooling • Serve as a technical lead and mentor to the team • Make every deployment smooth as our infrastructure evolves.

Role Description Quzara is seeking an AI/ML Engineer I to support the development, deployment, and operational support of AI-enabled applications serving federal and defense-adjacent customers. This role combines applied AI engineering with practical data science responsibilities to ensure reliable, secure, and high-performing AI systems in production environments. AI/ML Engineer will collaborate with internal development teams and government customers to troubleshoot issues, resolve defects, optimize model behavior, and improve data quality across AI-driven workflows. This is a hands-on engineering role focused on implementation, support, and continuous improvement not research or supervisory leadership. - 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 Qualifications - 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. Requirements - 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 Benefits - 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. EEO Statement The Company is an Equal Employment Opportunity (EEO) employer and does not discriminate based on race, color, religion, sex, sexual orientation, national origin, age, marital status, disability, veteran's status, or any other basis protected by applicable discrimination laws.