{"id":3752252,"url":"https://alion.io/job/material-depot-ml-engineer","title":"ML Engineer","company":{"id":3778465,"name":"Material Depot","domain":"materialdepot.in","url":"https://alion.io/company/material-depot","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["India"],"countries":["IN"],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Rest API","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"CI/CD","optional":true},{"name":"Computer Vision","optional":true},{"name":"Docker","optional":true},{"name":"GCP","optional":true},{"name":"Hugging Face","optional":true},{"name":"Kubeflow","optional":true},{"name":"Kubernetes","optional":true},{"name":"MLFlow","optional":true},{"name":"NLP","optional":true},{"name":"RAG","optional":true},{"name":"Recommender Systems","optional":true},{"name":"Transformers","optional":true}],"status":"live","first_seen_at":"2026-11-04T15:36:10Z","employer_posted_date":null,"last_verified_at":"2026-11-04T15:36:10Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"Responsibilities: \nDesign, develop, train, and deploy machine learning models for various business applications.\nBuild and optimize predictive models using supervised, unsupervised, and deep learning techniques.\nCollect, clean, preprocess, and analyze large datasets to identify patterns and meaningful insights.\nDevelop scalable data pipelines and feature engineering workflows to support ML model development.\nImplement machine learning algorithms using Python and relevant ML frameworks.\nEvaluate model performance using appropriate metrics and continuously improve accuracy, efficiency, and reliability.\nDeploy machine learning models into production environments using APIs, cloud platforms, and containerization tools.\nCollaborate with data scientists, backend engineers, and product teams to integrate ML solutions into existing applications.\nImplement model monitoring, versioning, retraining, and maintenance practices.\nExplore emerging machine learning technologies, frameworks, and methodologies to improve existing solutions.\nEnsure models are scalable, reliable, efficient, and aligned with business requirements.\n\nRequirements: \nBachelor's or master's degree in computer science, artificial intelligence, data science, engineering, or a related field.\n2-6 years of hands-on experience in machine learning, AI engineering, or a related technical role.\nDemonstrated experience developing and deploying machine learning models.\nStrong understanding of statistical concepts, data structures, and machine learning fundamentals.\nExperience working with large datasets and building scalable ML pipelines.\nAbility to work independently and collaborate effectively with distributed teams.\n\nMust Have: \nStrong programming experience in Python.\nMachine Learning Algorithms and Statistical Modelling.\nSupervised and Unsupervised Learning.\nDeep Learning and Neural Networks.\nData Processing and Feature Engineering.\nNumPy, Pandas, Scikit-learn.\nTensorFlow or PyTorch.\nModel Training, Evaluation, and Optimization.\nSQL and Database Management.\nData Structures and Algorithms.\nREST APIs and Model Deployment.\nProblem-Solving and Analytical Skills.\n\nGood-to-Have Skills: \nNatural Language Processing (NLP).\nGenerative AI and Large Language Models (LLMs).\nRetrieval-Augmented Generation (RAG).\nHugging Face and Transformers.\nMLOps and ML Model Lifecycle Management.\nDocker and Kubernetes.\nAWS, Azure, or Google Cloud Platform (GCP).\nMLflow, Kubeflow, or similar experiment-tracking tools.\nCI/CD and Automated Deployment Pipelines.\nComputer Vision and Recommendation Systems.\nSkills\nML, Machine Learning, PyTorch, 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