ML Engineer- W2

Job Title – Machine Learning Engineer Job Type – Remote Duration- 6+ months Job Description: Required Qualifications Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related field.8+ years of experience developing machine learning solutions in production environments.Strong proficiency in Python and software engineering best practices.Experience with machine learning frameworks such as:TensorFlowPyTorchScikit-learnXGBoostStrong understanding of supervised and unsupervised learning techniques.Experience building and managing data pipelines.Knowledge of SQL and large-scale data processing technologies.Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.Familiarity with containerization and orchestration tools such as Docker and Kubernetes.Experience with version control systems and CI/CD processes.Strong analytical, problem-solving, and communication skills.Preferred Qualifications Master's degree or PhD in a relevant technical field.Experience with large language models (LLMs), generative AI, or retrieval-augmented generation (RAG).Knowledge of MLOps tools such as MLflow, Kubeflow, SageMaker, or Vertex AI.Experience with distributed computing platforms such as Spark.Familiarity with vector databases and embedding technologies.Experience building AI-powered applications in a production environment.Knowledge of responsible AI, model governance, and explainability techniques.Technical Skills Programming PythonSQLJava, Scala, or C++ (preferred)Machine Learning & AI Deep LearningNatural Language Processing (NLP)Computer VisionPredictive ModelingGenerative AILLM Fine-TuningCloud & Infrastructure AWS, Azure, or GCPDockerKubernetesREST APIsCI/CDData Technologies SparkDatabricksAirflowFeature StoresData Warehousing SolutionsSuccess Metrics Model accuracy and performance improvementsDeployment reliability and scalabilityReduction in model latency and infrastructure costsBusiness impact generated through AI initiativesQuality and maintainability of production ML systemsOperational excellence through effective monitoring and automation