{"id":3202694,"url":"https://alion.io/job/amazon-machine-learning-engineer-2","title":"Machine Learning Engineer","company":{"id":19,"name":"Amazon","domain":"amazon.com","url":"https://alion.io/company/amazon","size_band":"1-10","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"B","score":78,"open_postings":4528,"ghost_share":0,"stale_share":0.89,"repost_share":0.002,"time_to_fill_p50_days":22,"computed_at":"2026-10-28T05:59:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":13000,"max_usd":34000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":18},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Amazon EC2","optional":true},{"name":"Amazon EKS","optional":true},{"name":"Amazon S3","optional":true},{"name":"Amazon SageMaker","optional":true},{"name":"AWS Lambda","optional":true},{"name":"Computer Vision","optional":true},{"name":"Kubeflow","optional":true},{"name":"MLFlow","optional":true}],"status":"live","first_seen_at":"2026-10-24T09:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-24T09:35:00Z","board_verified":false,"closed_at":null,"days_open":4,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":4},"description":"We are seeking a highly motivated Machine Learning Engineer to design, build, and deploy scalable machine learning solutions that enhance customer experiences and business operations. The ideal candidate will work closely with data scientists, software engineers, and product teams to develop production-grade ML systems capable of handling large-scale datasets.\n\nResponsibilities: \nDesign, develop, and deploy machine learning models for real-world business applications.\nBuild scalable ML pipelines for data preprocessing, training, validation, and deployment.\nDevelop predictive models, recommendation systems, NLP solutions, and classification algorithms.\nCollaborate with software engineering teams to integrate ML models into production environments.\nOptimise model performance, scalability, and reliability.\nMonitor deployed models and improve accuracy through continuous retraining and evaluation.\nWork with large-scale structured and unstructured datasets.\nConduct A/B testing and performance analysis of ML solutions.\nImplement MLOps best practices for model versioning, deployment, and monitoring.\nStay updated with emerging trends in AI, Machine Learning, and Generative AI technologies.\n\nRequirements: \nBachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or related field.\n2-5 years of experience in Machine Learning Engineering or related roles.\nStrong programming skills in Python.\nExperience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.\nStrong understanding of supervised and unsupervised learning algorithms.\nExperience with SQL and large-scale data processing.\nKnowledge of software engineering principles, data structures, and algorithms.\nExperience deploying ML models on cloud platforms such as AWS.\nFamiliarity with Docker, Kubernetes, and CI/CD pipelines.\n\nPreferred Qualifications: \nExperience with Large Language Models (LLMs) and Generative AI.\nKnowledge of NLP, Computer Vision, or Recommendation Systems.\nExperience with AWS services such as SageMaker, Lambda, S3 EC2 and EKS.\nExposure to MLOps tools such as MLflow, Kubeflow, or 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