AI Software Architect (AdTech)

Poznań - Poland

Monthly Salary: Not Disclosed

Posted on: 4 hours ago

Vacancies: 1 Vacancy

Job Summary

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.

Qualifications :

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.

Additional Information :

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

Remote Work :

Yes

Employment Type :

Full-time

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-...

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.

Qualifications :

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.

Additional Information :

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

Remote Work :

Yes

Employment Type :

Full-time

Key Skills

APIs
Spring
.NET
DevOps
Cloud Infrastructure
IaaS
AWS
Solution Architecture
Cloud Architecture
PaaS
Java
Azure

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Sigma Software

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