{"id":167697,"url":"https://alion.io/job/edwards-senior-engineer-ai-machine-learning","title":"Senior Engineer, AI & Machine Learning","company":{"id":5220,"name":"Edwards Lifesciences","domain":"edwards.com","url":"https://alion.io/company/edwards","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":81,"open_postings":61,"ghost_share":0,"stale_share":0.754,"repost_share":0.033,"time_to_fill_p50_days":26,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","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":{"min_usd":22000,"max_usd":50000,"period":"year","method":"role_seniority_country_cell","sample_n":37},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Machine Learning","optional":false},{"name":"RAG","optional":false},{"name":"Airflow","optional":true},{"name":"Apache Kafka","optional":true},{"name":"AWS","optional":true},{"name":"C++","optional":true},{"name":"Docker","optional":true},{"name":"Git","optional":true},{"name":"Hadoop","optional":true},{"name":"Java","optional":true},{"name":"Keras","optional":true},{"name":"Kubeflow","optional":true},{"name":"Kubernetes","optional":true},{"name":"MLFlow","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"PyTorch C++","optional":true},{"name":"Scikit-learn","optional":true},{"name":"Spark","optional":true},{"name":"TensorFlow","optional":true},{"name":"TensorFlow C++","optional":true}],"status":"live","first_seen_at":"2025-12-16T00:00:00Z","employer_posted_date":"2025-12-16","last_verified_at":"2026-10-17T20:11:08Z","board_verified":true,"closed_at":null,"days_open":306,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":306},"description":"Innovation starts from the heart. At Edwards Lifesciences, we’re dedicated to developing ground-breaking technologies with a genuine impact on patients’ lives. At the core of this commitment is our investment in cutting-edge information technology. This supports our innovation and collaboration on a global scale, enabling our diverse teams to optimize both efficiency and success. As part of our IT team, your expertise and commitment will help facilitate our patient-focused mission by developing and enhancing technological solutions.\nAI Factory team focuses in developing and delivering cutting edge technologies to improve patient care. Our vision is to provide proactive care and smart recovery to our patient community who are fighting cardiovascular disease through innovative connected solutions.\nIn this role you will be working alongside Product Engineering Leads in developing various desktop, web and mobile software fueled by a robust AI engine that will improve patient care. We look for candidates who are not only technically competent but can share the same pride we feel for the work we do to serve our patient community.\nHow you will make an impact:\nParticipate in agile development processes, including sprint planning and daily stand-ups\n\nCollaborate with cross-functional teams (data scientists, software engineers, product managers, business leads) to define requirements and deliver high-quality ML solutions.\n\nDevelop and implement end-to-end AI/ML models and workflows within the Palantir Foundry and AIP environment\n\nLeverage Palantir AIP features, such as Agent Studio, RAG (retrieval-augmented generation) workflows, and copilots, to ground models in relevant data sources and the Ontology.\n\nConduct research on open-source tools and ML techniques relevant to the medical domain\n\nDesign, implement, and optimize advanced machine learning algorithms to solve complex business problems.\n\nLead the development and deployment of scalable and efficient ML models in production environments.\n\nDrive research and experimentation to explore new ML techniques, tools, and frameworks.\n\nBuild end-to-end data pipelines for collecting, processing, and analyzing large-scale datasets.\n\nMentor junior engineers and contribute to the development of team processes and best practices.\n\nStay up-to-date with the latest trends and advancements in machine learning and AI.\n\nWhat you’ll need (Required)\nBachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or related field (PhD is a plus).\n\n5+ years of professional experience in machine learning engineering, with a strong focus on deploying machine learning models in production environments.\n\n1+ years of hands on experience with Palantir Foundry ecosystem and AIP\n\nProficiency in programming languages such as Python, Java, C++, or similar.\n\nExperience with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn, Keras, etc.).\n\nStrong understanding of machine learning algorithms, statistics, and data structures.\n\nExperience with cloud platforms such as AWS for model deployment and data storage.\n\nFamiliarity with big data technologies like Hadoop, Spark, or similar tools is a plus.\n\nSolid experience with version control systems (Git) and agile development methodologies.\n\nStrong communication skills and the ability to work effectively in cross-functional teams.\n\nWhat else we look for (Preferred)\nPreferred certifications include Foundry Data Engineer Certification, Foundry Solution Architect Certification, or Foundry Application Developer Certification..\n\nExperience with deep learning techniques (e.g., CNNs, RNNs, GANs, etc.).\n\nFamiliarity with MLOps and tools for model deployment and monitoring (e.g., MLflow, Kubeflow, Docker, Kubernetes).\n\nKnowledge of data engineering practices and tools like Apache Kafka, Airflow, 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