Machine Learning Engineer 4

**What You ll Do:** - Develop classifiers, predictive models and multi variate optimization algorithms on large-scale datasets using advanced statistical modeling, machine learning and data mining. - Design, implement and operate scalable models that can work with large-scale datasets (100s billions of records) in production systems. Ability to articulate the design and implementation choices to cross functional teams - R&D will revolve around a few key focus areas such as Agentic AI solutions, predictive models for conversion optimization, Reinforcement Learning, and Forecasting & Planning. - Model Lifecycle Management: Manage model versioning, deployment strategies, rollback mechanisms, and A/B testing frameworks. - Coordinate model registries, artifacts, and promotion workflows in collaboration with ML Engineers Develop CI/CD and orchestration workflows using GitLab CI, GitHub Actions, CircleCI, Airflow, Argo Workflows, or similar tools. - Review and optimize data science models, including code refactoring, containerization, deployment, versioning, and performance tuning. - Implement model testing, validation, and automated QA pipelines, ensuring reproducibility and compliance. - Monitor models in production, including data drift, concept drift, performance degradation, and system reliability. - Collaborate multi-functionally with data scientists, data engineers, and architects; build documentation and improve team processes. - Ensure governance, security, and compliance for ML pipelines (access controls, audit logs, model reproducibility, lineage). **What you require:** - 7-9 yrs. of relevant experience as ML engineer - Strong programming skills in Python, Java/Scala, SQL, Hive, Spark Experience working on production systems involving machine learning, NLP, classifiers, statistical modeling and multivariate optimization techniques, GenAI/LLM/Agentic  solutions. - Hands-on experience with MLOps frameworks like MLflow, Kubeflow, Airflow or similar. - Experience with control systems, reinforcement learning problems, contextual bandit algos - Experience with common ML libraries such as scikit-learn, TensorFlow, Keras, PyTorch. - Experience with software engineering guidelines including version control, testing, and automation. - Experience with observability tools (Prometheus, Grafana, ELK, CloudWatch, Datadog) - Knowledge of cloud services such as AWS Sagemaker, Azure ML, GCP Vertex AI. - Knowledge of Docker, Kubernetes (EKS/GKE/AKS), and enterprise platforms like OpenShift. - Familiarity with infrastructure-as-code (Terraform, CloudFormation) - Strong ability to design and implement cloud architectures for end-to-end ML workflows on AWS. - Ability to understand data science workflows, experiment tracking, and featureengineering tools. - Strong communication skills; ability to work collaboratively in multi functional teams & articulate the design and implementation choices to cross functional teams. - General understanding of data structures, algorithms, multi-threaded programming anddistributed computing concepts - Ability to be a self-starter and work closely with other data scientists and softwareengineers to design, test and build production ready ML and optimization models anddistributed algorithms running on large scale data sets - Strong analytical, quantitative problem solving, and communication skills - Proven ability to work well in a high performing team with agile developmentapproaches and technolog Note: This is a third party job (Aggregated by careeruplift.ai). Shortlisting and Final hiring decision & process is handled by the company.