Senior Machine Learning Engineer

About the team:

Key responsibilities:

• Possess deep knowledge of core systems and take full responsibility for the code, design, and project delivery, ensuring high quality and on-time completion
• Build and evolve end-to-end ML lifecycle systems from data processing to model serving — for journey prediction, churn/conversion propensity, and intervention-matching models — using technologies spanning big data frameworks to ML platforms (TensorFlow, PyTorch)
• Design and implement sophisticated monitoring systems that track both technical health and model quality metrics (prediction accuracy, drift, intervention lift)
• Create advanced experimentation frameworks that enable rapid A/B testing of journey-prediction algorithms and intervention strategies
• Design and develop low-latency, high-throughput serving systems with appropriate caching strategies to support real-time intervention decisions
• Establish MLOps best practices using MLflow for experiment tracking, model versioning, and deployment automation
• Build stream processing pipelines for real-time feature computation on subscriber behavior and lifecycle signals that power dynamic journey personalization and next-best-action decisioning
• Scale and optimize training and serving of machine learning models, including specialized solutions for embedding scaling
• Collaborate with Product Managers and cross-functional teams to understand requirements and deploy high-quality software solutions that meet business objectives
• SKILLS & ATTRIBUTES FOR SUCCESS:
• Strong coding skills in Python, Java, Golang, or Scala
• Experience with Big Data frameworks like Spark and Kubernetes for data and ML workloads
• Proficient in implementing API performance monitoring solutions using tools such as Grafana, Prometheus, and Cloudwatch
• Experience with machine learning libraries including TensorFlow, PyTorch, JAX or Keras
• Skilled in developing end-to-end machine learning pipelines ensuring consistency between development and production environments
• Ability to design scalable ML architectures tailored to site traffic and feature complexity for predictive algorithms
• Familiar with model and data versioning, resource allocation and scaling, and logging to build optimal systems
• Experienced in creating systems that monitor and react to faults in resources, data streams, and model responses
• Expertise with MLOps tools for scalable, production-level deployment, including feature stores, model hosting and versioning, data versioning, prediction and drift monitoring, and automated remediation
• Strong problem-solving skills and the ability to work effectively in a fast-paced environment
• Commitment to continuous learning and professional development
• PREFERRED EDUCATION & EXPERIENCE:
• B.Tech/B.E/ Masters or equivalent degree. At least 4+ years of professional experience in software engineering roles, with a track record of delivering high-quality projects independently.

ABOUT US:

Qualifications

• Experience with Big Data frameworks like Spark and Kubernetes for data and ML workloads
• Experience with machine learning libraries including TensorFlow, PyTorch, JAX or Keras
• Skilled in developing end-to-end machine learning pipelines ensuring consistency between development and production environments