Overview

Role : Consultant - Machine Learning Engineer

Responsibilities

• Design, build, and operate production ML pipelines covering feature engineering, model training, serving, monitoring, and retraining.
• Build and maintain scalable, low-latency ML model serving infrastructure for real-time and batch inference.
• Own ML CI/CD processes including model versioning, automated retraining, deployments, canary releases, and rollback strategies.
• Build robust batch and streaming feature pipelines using technologies such as Spark and Kafka.
• Implement observability, data-quality checks, drift detection, SLA monitoring, and alerting for ML systems.
• Work closely with Data Scientists and ML Specialists to productionize research prototypes into scalable and maintainable services.
• Optimize ML infrastructure for performance and cost, including GPU utilization, batching, caching, and model optimization.
• Contribute to architecture and platform decisions for MLOps tooling, ML infrastructure, and distributed systems.
• Participate in production support, incident response, postmortems, and reliability improvements.
• Mentor engineers and lead technical design and architecture reviews for ML platform and serving components.

Qualifications

• 6 - 9 years of software engineering experience with 4+ years of experience building and operating production ML systems.
• Strong hands-on expertise in Java, Scala, or Python with good knowledge of software engineering practices, testing, design patterns, and code quality.
• Strong experience with AWS cloud services including EC2, EKS/Kubernetes, S3, Lambda, and IAM.
• Experience with Kafka, Spark, distributed systems, event-driven pipelines, and scalable microservices.
• Hands-on experience with ML serving frameworks such as TorchServe, TensorFlow Serving, Triton, or custom/gRPC-based services.
• Experience with MLOps and CI/CD tools such as MLflow, SageMaker Pipelines, Airflow, Jenkins, GitHub Actions, or similar platforms.
• Strong understanding of data validation, unit/integration testing, model testing, and prevention of train/serve skew.
• Experience with Terraform or similar infrastructure-as-code tools, Docker, and Kubernetes.
• Knowledge of ML fundamentals and the ability to collaborate effectively with Data Scientists and ML teams.
• Experience owning production systems, including SLAs, on-call support, incident management, and capacity planning.
• Opportunity to work on large-scale production ML and MLOps systems.
• Exposure to advanced AI, cloud, distributed systems, and Generative AI technologies.
• Opportunity to work with experienced Data Scientists, ML Engineers, and technology professionals.
• Scope to contribute to architecture, platform engineering, and high-impact ML initiatives.
• Opportunities for technical leadership, mentoring, and continuous learning.