{"id":3214756,"url":"https://alion.io/job/altas-partners-ai-ml-lead","title":"AI / ML Lead","company":{"id":4471759,"name":"Altas Partners","domain":"altas.com","url":"https://alion.io/company/altas-partners","size_band":"201-500","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":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Noida, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":22000,"max_usd":58000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":47},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon 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practices for the ML lifecycle, including training, validation, deployment, versioning, and monitoring.\nLead architecture decisions for model scaling, performance optimisation, and reliability.\nCollaborate closely with data engineers and platform teams to ensure seamless integration.\nEstablish standards for reusability, modularity, and maintainability of AI/ML components.\nGuide the adoption of feature stores, model registries, and data lakes.\nMentor senior data scientists and ML engineers on solution design, architecture patterns, and production readiness.\nReview technical designs, conduct architecture walkthroughs, and provide technical governance.\n\nThe core requirements for the job include the following:\n\nCore AI/ML Skills: \nStrong proficiency in Python.\nHands-on experience with TensorFlow and/or PyTorch.\nDeep understanding of deep learning architectures and model optimisation.\nExperience with distributed data processing using Spark.\n\nMLOps and Platform: \nStrong knowledge of MLOps practices (CI/CD for ML, model versioning, monitoring, retraining).\nExperience with model registries, feature stores, and experiment tracking tools.\nExposure to data lakes and large-scale data platforms.\n\nCloud and Tools: \nHands-on experience with cloud AI platforms such as AWS Bedrock and SageMaker, Azure Machine Learning, and Google Cloud AI / Vertex AI.\nStrong understanding of cloud-native architectures and scalable ML deployments.\nSkills\nMLOps, PyTorch, Python, SageMaker, Spark, TensorFlow, Vertex 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