Machine Learning Expert

4 days ago

amravati, India beBeeMachineLearningEngineer Full time

Job OverviewWe are seeking a highly skilled Machine Learning Engineer to design, build, deploy, and optimize ML/AI solutions across various business problems. The ideal candidate will have a strong understanding of the full ML lifecycle, including data pre-processing, experimentation, deployment, monitoring, and continuous improvement.Key ResponsibilitiesModel Development & ExperimentationBuild, train, and evaluate machine learning models (classification, regression, clustering, NLP, deep learning).Develop custom models or adapt pre-trained models (transfer learning).Conduct feature engineering, feature selection, and data transformation.Perform hyperparameter tuning using tools like Optuna, Ray Tune, or built-in frameworks.Evaluate model performance using appropriate metrics (AUC, F1, MAE, BLEU, WER, etc.).Data Engineering & ProcessingWork with structured and unstructured data (text, images, audio, logs, documents).Build data pipelines using Python, SQL, Spark, Airflow, or similar.Apply data quality checks, deduplication, anomaly detection, and data validation (Great Expectations, pydantic).ML Deployment & MLOpsDeploy models as APIs using FastAPI, Flask, Node, or cloud-native tools.Implement CI/CD pipelines for ML (GitHub Actions, GitLab, Jenkins).Use containerization & orchestration (Docker, Kubernetes).Manage models using MLflow, Vertex AI, SageMaker, Azure ML, or Databricks.Implement automated retraining, model versioning, rollback, and monitoring.Handle inference optimizations (quantization, batching, caching).LLM & GenAI (Preferred)Work with foundation models like GPT, LLaMA, Claude, Mistral, etc.Build RAG pipelines with vector databases (Qdrant, Pinecone, FAISS, Elasticsearch).Prompt engineering and evaluation.Optimize embeddings, context windows, and chunking strategies.Integrate LLMs into production APIs and workflows.Cloud & InfrastructureExperience with at least one cloud provider (AWS, Azure, GCP).Use managed ML services like: AWS: SageMaker, Lambda, Textract, Comprehend Azure: AI Foundry, Functions, Search GCP: Vertex AI, Cloud RunManage GPU/CPU compute workloads efficiently.Software Engineering Best PracticesWrite clean, modular, well-tested Python code.Maintain repositories adhering to branching, versioning, and code review standards.Use design patterns for scalable ML pipelines.Build internal tools for automation, testing, and monitoring.Cross-functional CollaborationWork closely with product managers, data engineers, backend engineers, and business analysts.Translate business problems into ML solutions with measurable ROI.Document technical designs, APIs, models, architecture diagrams.Monitoring, Observability & OptimizationImplement model monitoring for data drift, concept drift, performance degradation.Evaluate model reliability, bias, fairness.Implement logging & metrics (Prometheus, Grafana, ELK).Continuously improve model performance and resource usage.

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