ML Eng 4 Yrs

About The Opportunity We operate in the Information Technology & Services sector, focused on AI/ML product engineering and cloud-native data solutions for enterprise and SMB clients. Our teams design, build, and deploy production-grade machine learning systems that drive business outcomes across analytics, automation, and intelligent products. This remote role (India) is an opportunity to join a fast-moving engineering organization delivering end-to-end ML solutions—model development, CI/CD-driven deployment, monitoring, and continual improvement—across cloud environments. Role & Responsibilities Design, implement, and productionize ML models and inference pipelines with reliability, scalability, and observability in mind. Develop clean, well-tested Python services for data preprocessing, feature engineering, model training, and inference. Containerize models and services using Docker and integrate with cloud-hosted deployment pipelines on AWS. Collaborate with data scientists and product teams to translate proofs-of-concept into production-ready systems and APIs. Implement MLOps best practices—automated training, model versioning, deployment strategies, monitoring, and alerting. Drive performance tuning and optimization for model latency, throughput, and cost across production workloads. Skills & Qualifications Must-Have Python PyTorch TensorFlow Scikit-learn SQL Docker AWS Preferred MLflow Kubernetes Apache Spark Qualifications Bachelor's degree in Computer Science, Engineering, Mathematics, or equivalent practical experience. Approximately 4 years of hands-on experience in machine learning engineering or related software engineering roles. Proven track record deploying ML models to production and maintaining model lifecycle (training, versioning, monitoring). Comfort working in remote, cross-functional teams and communicating technical trade-offs to stakeholders. Benefits & Culture Highlights Fully remote role with flexible working hours and India-based hiring. Opportunity to own end-to-end ML features and grow into senior technical roles across product & cloud engineering. Learning budget and time for upskilling in MLOps, cloud platforms, and advanced ML techniques. Primary role title (standardized): Machine Learning Engineer To apply, candidates should be prepared to share code samples, model artefacts, and examples of deployed ML systems. Skills: nlp,ml,llm