AI Software Architect (AdTech)

We are looking for a Software Architect with a strong focus on AI/ML to design and implement cutting-edge generative AI solutions. In this position, you will take full ownership of technical design decisions, define AI architecture strategy, and ensure the successful integration of advanced AI models into a complex AdTech platform.

Become an AI trailblazer in a growing department with executive support. Drive innovation in a mature, well-established platform while collaborating with industry leaders and international teams. From day one, build a scalable AI architecture with full ownership and support.

This is a unique opportunity combines classic architectural leadership with modern AI technologies to drive product innovation and make a tangible impact on the advertising industry.

Customer
Our Customer is a Swedish AdTech company that transforms the way publishers and advertisers interact. They provide state-of-the-art self-serve advertising platforms that enable seamless, direct transactions between advertisers and some of the world’s largest publishers. Their mission is to make advertising more accessible, automated, and efficient through innovative technology. Together we serve globally recognized clients such as TripAdvisor, Bloomberg, The Washington Post, Opera, Dow Jones, etc. 

At Sigma Software, we collaborate with industry leaders like our Сustomer to push the boundaries of AI-driven automation in AdTech. As we continue to grow, we are looking for top talents to help shape the future of advertising with cutting-edge AI solutions. Join us in building intelligent, scalable, and impactful products in a fast-paced, dynamic environment. 

Project
The project is a scalable, white-label ad platform used by major global publishers. It integrates AI-driven automation to enhance advertising transactions, targeting efficiency, and personalization. As part of this initiative, you’ll design AI architecture from the ground up, ensuring production-ready, scalable solutions.

• AI Strategy & Integration: Design, train, and deploy production-grade ML models (using AWS SageMaker, Bedrock, Comprehend) for predictive analytics, anomaly detection, recommendation engines, and automation.
• MLOps & Model Serving: Own the entire ML model lifecycle, from training and versioning to secure, low-latency deployment (Model Serving) on AWS.
• Data Strategy & Governance: Define and implement a company-wide data strategy, including governance, quality, lineage, and access control.
• Partner with the Domain Expert to formalize AI-driven rules within the BRD.
• AI Governance: Implement robust monitoring for model drift, bias, and performance.
• Explainability (XAI): Develop and expose "explainability" endpoints for critical AI decisions (e.g., campaign rejection) to support governance and the Agentic Economy.
• Collaboration: Work with the Platform Back-End Dev to define data pipelines and API contracts for real-time model inference.

• Leadership: Proven experience building and leading centralized Data/AI teams, including data engineering, data science, and MLOps functions.
• Cloud MLOps: 5+ years of experience deploying ML models into production on AWS (SageMaker, MLOps pipelines).
• SaaS AI Deployment: Experience deploying AI-powered features into customer-facing SaaS platforms, from recommender systems to intelligent assistants.
• API Serving: Proven expertise in exposing ML inference via scalable, low-latency REST APIs.
• Data Pipelines: Strong background designing and managing high-volume data pipelines (e.g., Kinesis, Kafka, Spark) for feature engineering.
• Model Governance: Experience implementing model monitoring, drift detection, and XAI frameworks, including establishing an AI governance framework with model inventory, explainability standards, and ethical review processes.
• Statistical Modeling: Advanced degree (MS/PhD) in Computer Science, Statistics, or a quantitative field.
• Strategy: Demonstrated ability to translate AI capabilities into product strategy, driving alignment with Product, Engineering, and Business teams.
• Data Security & Privacy: Deep understanding of data security, PII handling, and privacy-by-design (GDPR, CCPA, SOC2) as applied to ML systems.
• Upper-Intermediate level of English or higher
   

Preferred Qualifications:

• AWS Certified Machine Learning Specialty.
• NLP & GenAI: Experience with NLP for content analysis (e.g., creative validation) or LLM integration, including LLMOps, GenAI safety, prompt versioning, and evaluation.
• Experience with probabilistic programming or causal inference.

Personal profile:

• Strong leadership and an ownership mindset
• Ability to innovate in mature, large-scale systems
• Comfortable working in international, cross-functional teams
• Passion for AI/ML and emerging technologies