Senior AI/ML Engineer

About Zinkworks

At Zinkworks, we’re transforming industries by creating cutting-edge solutions that fuse AI, ML, and intelligent automation into real-world applications. We're passionate about innovation, quality, and building high-performance teams that drive impactful change across telecommunications, finance, and emerging tech.

About the Role

We are looking for an experienced and motivated AI/ML Engineer to join our Solutions & Innovation team. This role involves designing and implementing AI models for proof-of-concepts and project solutions within two key verticals: capital markets and telecom networks. You’ll work on a diverse range of use cases, including anomaly detection in RAN (Radio Access Network) time-series data, network optimization, and time-series modeling in financial systems. Your work will be instrumental in helping our clients, ranging from telecom operators to global banks, leverage advanced AI/ML capabilities for real-time insights and predictive intelligence.

Key Responsibilities

Model Development & Experimentation

• Design, build, and refine AI/ML models using architectures such as LSTM, Transformer, and GCN (Graph Convolutional Networks.)
• Apply anomaly detection techniques (e.g., Isolation Forest, statistical methods) to multivariate time-series data.

Data Engineering & Processing

• Work with structured and semi-structured data from telecom and financial systems.
• Develop data ingestion and transformation pipelines using GCP services (e.g., Cloud Functions, BigQuery).

Deployment & Integration

• Deploy scalable models to production using GCP tools such as Vertex AI and Cloud Run.
• Develop REST APIs to expose inference services for integration with client systems.

Collaboration & Delivery.

• Collaborate with cross-functional teams, including solution architects, cloud engineers, and domain experts.
• Participate in client workshops and technical governance for PoC delivery and feedback incorporation.

Monitoring & Validation

• Define and track model evaluation metrics (e.g., F1-score, ROC-AUC, RMSE).
• Implement logging, monitoring, and retraining strategies as needed.

Required Skills and Experience

• Strong proficiency in Python, with experience using ML frameworks such as TensorFlow or PyTorch
• Solid understanding of time-series modeling, anomaly detection, and multivariate data analysis
• Hands-on experience with Google Cloud Platform (GCP)—especially Vertex AI, BigQuery, Cloud Functions, and Cloud Run
• Familiarity with RESTful API development and serving ML models in production

Preferred Qualifications.

• Experience working with one or more of the following
• Telecom KPIs (e.g., latency, throughput, jitter, S INR)Capital markets datasets (e.g., trade logs, FIX message flows)
• Experience with Graph Neural Networks and Transformer-based models.
• Exposure to OSS/BSS systems, RAN telemetry, or network optimization domains.
• Familiarity with containerization and CI/CD workflows (e.g., Docker, GitHub Actions).
• Prior experience in financial services, especially around trade surveillance or back-office analytics.