Point Wild (Formerly Pango Group)

AI Engineer

• Design, implement, and optimize machine learning models (supervised, unsupervised, NLP-based) to drive security-focused intelligence.
• Build and deploy RAG pipelines, embeddings, and feature stores for context-aware, real-time decision-making.
• Collaborate with Data and MLOps engineers to package, deploy, and monitor models in scalable, cloud-native environments (CI/CD).
• Leverage deep learning and NLP to provide actionable insights into risk posture, compliance, and security events.
• Research and prototype AI approaches that can adapt to post-quantum cryptographic standards and automated remediation.
• Implement, refine, and maintain ML models, perform performance tuning, and support multiple parallel AI initiatives.

• Advanced AI Model Development: Designing and optimizing machine learning models (supervised, unsupervised, and NLP-based) to drive security-focused intelligence across the platform.
• RAG & Embedding Workflows: Building and deploying RAG pipelines, embeddings, and feature stores that enable context-aware, real-time decision-making.
• Production-Ready AI Systems: Collaborating with Data and MLOps engineers to package, deploy, and monitor models in scalable, cloud-native environments.
• AI-Driven Analytics: Leveraging deep learning and NLP to provide actionable insights into risk posture, compliance, and security events.
• Future-Proofing for Quantum: Researching and prototyping AI approaches that can adapt to post-quantum cryptographic standards and automated remediation.
• Model Development & Optimization: Implement, refine, and maintain ML models (supervised, unsupervised, NLP-based models).
• Production Integration: Work with Data/MLOps engineers to package, deploy, and monitor models within a robust CI/CD pipeline.
• Performance Tuning: Optimize model efficacy, efficiency, and reliability.
• Parallel Project Support: Split or manage multiple AI initiatives to enable faster execution across various R&D efforts.

• Design and optimize machine learning models (supervised, unsupervised, and NLP-based) to drive security-focused intelligence across the Lat61 platform.
• Build and deploy RAG pipelines, embeddings, and feature stores for context-aware, real-time decision-making.
• Collaborate with Data and MLOps engineers to package, deploy, and monitor models in scalable, cloud-native environments.
• Leverage deep learning and NLP to provide actionable insights into risk posture, compliance, and security events.
• Research and prototype AI approaches adaptable to post-quantum cryptographic standards and automated remediation.
• Implement, refine, and maintain ML models; optimize model efficacy, efficiency, and reliability; support multiple parallel AI initiatives.

Data Engineer

• Design, build, and optimize data ingestion pipelines on Databricks for batch and streaming data.
• Implement scalable data models and transformations leveraging Delta Lake and open data formats (Parquet, Delta).
• Design and manage workflows with Databricks Workflows, Airflow, or equivalent orchestration tools.
• Implement automated testing, lineage, and monitoring frameworks using tools like Great Expectations and Unity Catalog.
• Build integrations with enterprise and third-party systems via cloud APIs, Kafka/Kinesis, and connectors into Databricks.
• Partner with AI/ML teams to provision feature stores, integrate vector databases, and support RAG-style architectures.
• Optimize Spark and SQL workloads for speed and cost efficiency across multi-cloud environments (AWS, Azure, GCP).
• Apply secure-by-design data engineering practices aligned with Point Wild’s cybersecurity standards and post-quantum cryptographic frameworks.
• Collaborate closely with data architects, AI engineers, and product leaders to deliver a scalable, resilient, and secure foundation for analytics, ML, and cryptographic risk management.

• Build and optimize data ingestion pipelines on Databricks (batch and streaming) to process structured, semi-structured, and unstructured data.
• Implement scalable data models and transformations leveraging Delta Lake and open data formats (Parquet, Delta).
• Design and manage workflows with Databricks Workflows, Airflow, or equivalent orchestration tools.
• Implement automated testing, lineage, and monitoring frameworks using tools like Great Expectations and Unity Catalog.
• Build integrations with enterprise and third-party systems via cloud APIs, Kafka/Kinesis, and connectors into Databricks.
• Partner with AI/ML teams to provision feature stores, integrate vector databases (Pinecone, Milvus, Weaviate), and support RAG-style architectures.
• Optimize Spark and SQL workloads for speed and cost efficiency across multi-cloud environments (AWS, Azure, GCP).
• Apply secure-by-design data engineering practices aligned with Point Wild’s cybersecurity standards and post-quantum cryptographic frameworks.
• Collaborate closely with data architects, AI engineers, and product leaders to deliver a scalable, resilient, and secure foundation for analytics and ML.

• Build and optimize data ingestion pipelines on Databricks (batch and streaming) to process structured, semi-structured, and unstructured data.
• Implement scalable data models and transformations leveraging Delta Lake and open data formats (Parquet, Delta).
• Design and manage workflows with Databricks Workflows, Airflow, or equivalent orchestration tools.
• Implement automated testing, lineage, and monitoring frameworks using tools like Great Expectations and Unity Catalog.
• Build integrations with enterprise and third-party systems via cloud APIs, Kafka/Kinesis, and connectors into Databricks.
• Partner with AI/ML teams to provision feature stores, integrate vector databases (Pinecone, Milvus, Weaviate), and support RAG-style architectures.
• Optimize Spark and SQL workloads for speed and cost efficiency across multi-cloud environments (AWS, Azure, GCP).
• Apply secure-by-design data engineering practices aligned with Point Wild’s cybersecurity standards and evolving post-quantum cryptographic frameworks.

• Build and optimize data ingestion pipelines on Databricks (batch and streaming) to process structured, semi-structured, and unstructured data.
• Implement scalable data models and transformations leveraging Delta Lake and open data formats (Parquet, Delta).
• Design and manage workflows with Databricks Workflows, Airflow, or equivalent orchestration tools.
• Implement automated testing, lineage, and monitoring frameworks using tools like Great Expectations and Unity Catalog.
• Build integrations with enterprise and third-party systems via cloud APIs, Kafka/Kinesis, and connectors into Databricks.
• Partner with AI/ML teams to provision feature stores, integrate vector databases (Pinecone, Milvus, Weaviate), and support RAG-style architectures.
• Optimize Spark and SQL workloads for speed and cost efficiency across multi-cloud environments (AWS, Azure, GCP).
• Apply secure-by-design data engineering practices aligned with Point Wild’s cybersecurity standards and evolving post-quantum cryptographic frameworks.
• Collaborate closely with data architects, AI engineers, and product leaders to deliver a scalable, resilient, and secure foundation for analytics and ML.