Overview

A rapid-scaling AI/ML solutions partner in the enterprise tech sector, we architect and deploy production-grade machine learning systems for global clients across fintech, healthcare, and logistics. Our engineers solve high-impact business problems using cutting-edge ML models from recommendation engines and anomaly detection to NLP-driven automation deployed via scalable cloud infrastructure and MLOps pipelines.

Responsibilities

• Design, train, and deploy supervised/unsupervised ML models using Python, scikit-learn, TensorFlow, or PyTorch to solve real-world client problems.
• Build end-to-end data pipelines from ingestion to feature engineering to model serving using pandas, Spark, or Dask for large-scale datasets.
• Collaborate with data engineers and product teams to integrate ML models into production systems via REST APIs or containerized services (Docker/K8s).
• Optimize model performance through hyperparameter tuning, cross-validation, and A/B testing frameworks to ensure business impact.
• Implement MLOps best practices: model versioning (MLflow), monitoring (Prometheus/Grafana), and CI/CD automation for model retraining.
• Translate business KPIs into ML evaluation metrics and communicate technical outcomes to non-technical stakeholders.

Qualifications

• Must-Have skills: Python, scikit-learn, TensorFlow, PyTorch, pandas, SQL, REST API, Git
• Preferred skills: MLflow, Docker, Kubernetes

Nice to have

• MLflow
• Docker
• Kubernetes

Benefits

• Work with Fortune 500 clients and tackle mission-critical AI problems from day one.
• Access to cutting-edge tech stack and hands-on project ownership in agile, cross-functional teams.
• Opportunities for upskilling via internal AI labs, certification sponsorships, and conference participation.