Senior Machine Learning Engineer
Tools
Python, SQL, AWS / GCP / Azure, Spark or Databricks, Docker, Kubernetes, Kafka (or Kinesis / Flink), Terraform.
Frameworks
PyTorch and/or TensorFlow (deep learning), along with MLOps practices for model deployment, serving, and monitoring.
Type of Project
Design and productionization of Machine Learning solutions at scale: ML/DL models, batch and real-time data pipelines, and the services and infrastructure to operate them reliably. Applied across domains such as predictive modeling, recommendation systems, computer vision, NLP, and information retrieval — with GenAI (LLMs / RAG / agents) as a plus.
Senior Full Stack (Python / React)
Tools
JavaScript, HTML/DOM, CSS, AJAX, Python, SQL, Redis, gRPC, Google Cloud (Firestore, Cloud Storage, Pub/Sub, Artifact Registry, Cloud Run, BigQuery, Cloud Composer), Kubernetes, Terraform, New Relic, PagerDuty.
Frameworks
React on the front-end; Flask / FastAPI with REST APIs on the back-end.
Type of Project
Architecting and building scalable backend microservices for a Consumer Lending platform that handles millions of daily requests, without compromising security or performance. Strong focus on distributed systems on Google Cloud, cross-functional work with product, operations, and compliance, and fintech domain: payment systems, financial disbursement, and compliance-driven platforms (security, KYC/AML). Architectural patterns like Hexagonal and Event-Driven, plus A/B testing, feature flags, and gradual rollouts.