Staff AI Developer
Thermo Fisher Scientific
Staff AI Developer
Job Description
Work ScheduleStandard (Mon-Fri)Environmental ConditionsOfficeJob DescriptionAs part of the Thermo Fisher Scientific team, you’ll discover meaningful work that makes a positive impact on a global scale. Join our colleagues in bringing our Mission to life every single day to enable our customers to make the world healthier, cleaner and safer. We provide our global teams with the resources needed to achieve individual career goals while helping to take science a step beyond by developing solutions for some of the world’s toughest challenges, like protecting the environment, making sure our food is safe or helping find cures for cancer.DESCRIPTION:Join our AI team where you'll design and implement artificial intelligence solutions that help make the world healthier, cleaner, and safer. As a Staff Engineer in Artificial Intelligence, you'll architect and develop advanced AI/ML solutions that drive innovation across our diverse business units. You'll collaborate with data scientists, researchers, and business stakeholders to create scalable AI architectures, optimize model performance, and implement state-of-the-art machine learning solutions. This role includes developing predictive models for patient and donor risk, working with healthcare datasets, and integrating AI capabilities into applications, often leveraging cloud-based platforms.Key Responsibilities Design, develop, and deploy AI/ML models and algorithms Build predictive models for patient and donor risk assessment (e.g., eligibility, adverse outcomes, fraud/risk scoring) Work with structured and unstructured healthcare/life sciences data (EHR, clinical, donor data) Perform data preprocessing, feature engineering, and model evaluation Integrate AI models into production systems via APIs or microservices Collaborate with data scientists, clinicians, and business stakeholders Optimize model performance, accuracy, and interpretability Utilize cloud AI/ML platforms (AWS, Azure, Google Cloud) Monitor, retrain, and maintain models in production (MLOps practices) Ensure compliance with healthcare regulations (HIPAA, FDA, GDPR as applicable) Apply ethical AI practices, including bias detection and model transparency Stay updated with advancements in AI, healthcare analytics, and risk modeling Required Qualifications Bachelor’s or Master’s degree in computer science, AI, Data Science, or related field 5+ years of experience in AI/ML development Strong programming skills in Python (preferred), C#.Net, Java, or C++ Experience with machine learning frameworks: TensorFlow, PyTorch, Scikit-learn Experience developing risk models (classification, regression, survival analysis) Familiarity with Natural language processing (NLP) framework Knowledge of healthcare or life sciences data, including patient and donor datasets Experience with data preprocessing and analysis (Pandas, NumPy) Familiarity with REST APIs and software development practices Understanding of statistics, probability, and data structures Experience with version control (GitHub) Strong problem-solving and analytical skills Good communication and collaboration abilities Preferred Qualifications • Experience with Generative AI and large language models (LLMs) • Familiarity with healthcare data standards (HL7, FHIR) • Experience with cloud AI services: • AWS SageMaker • Azure AI / ML Studio • Google Vertex AI • Knowledge of MLOps tools (MLflow, Kubeflow, Docker, Kubernetes) • Experience with big data tools (Spark, Kafka) • Background in clinical analytics, epidemiology, or biostatistics • AI or cloud certifications
Locations
- India, Bangalore
Skills Required
- AI/ML developmentintermediate
- machine learning frameworks: TensorFlowintermediate
- Natural language processingintermediate
- healthcareintermediate
- data preprocessingintermediate
- REST APIsintermediate
- version controlintermediate
- Generative AIintermediate
- healthcare data standardsintermediate
- cloud AI servicesintermediate
- MLOps toolsintermediate
- big data toolsintermediate
- clinical analyticsintermediate
Required Qualifications
- Bachelor’s or Master’s degree in computer science, AI, Data Science, or related field (degree in master)
- Bachelor’s or Master’s degree in computer science, AI, Data Science, or related field (degree in master)
- 5+ years of experience in AI/ML development (experience, 5 years)
- 5+ years of experience in AI/ML development (experience, 5 years)
- Strong programming skills in Python (preferred), C#.Net, Java, or C++ (experience)
- Strong programming skills in Python (preferred), C#.Net, Java, or C++ (experience)
- Experience with machine learning frameworks: TensorFlow, PyTorch, Scikit-learn (experience)
- Experience with machine learning frameworks: TensorFlow, PyTorch, Scikit-learn (experience)
- Experience developing risk models (classification, regression, survival analysis) (experience)
- Experience developing risk models (classification, regression, survival analysis) (experience)
- Familiarity with Natural language processing (NLP) framework (experience)
- Familiarity with Natural language processing (NLP) framework (experience)
- Knowledge of healthcare or life sciences data, including patient and donor datasets (experience)
- Knowledge of healthcare or life sciences data, including patient and donor datasets (experience)
- Experience with data preprocessing and analysis (Pandas, NumPy) (experience)
- Experience with data preprocessing and analysis (Pandas, NumPy) (experience)
- Familiarity with REST APIs and software development practices (experience)
- Familiarity with REST APIs and software development practices (experience)
- Understanding of statistics, probability, and data structures (experience)
- Understanding of statistics, probability, and data structures (experience)
- Experience with version control (GitHub) (experience)
- Experience with version control (GitHub) (experience)
- Strong problem-solving and analytical skills (experience)
- Strong problem-solving and analytical skills (experience)
- Good communication and collaboration abilities (experience)
- Good communication and collaboration abilities (experience)
Preferred Qualifications
- • Experience with Generative AI and large language models (LLMs) (experience)
- • Familiarity with healthcare data standards (HL7, FHIR) (experience)
- • Experience with cloud AI services: (experience)
- • Azure AI / ML Studio (experience)
- • Google Vertex AI (experience)
- • Knowledge of MLOps tools (MLflow, Kubeflow, Docker, Kubernetes) (experience)
- • Experience with big data tools (Spark, Kafka) (experience)
- • Background in clinical analytics, epidemiology, or biostatistics (experience)
- • AI or cloud certifications (certification)
Responsibilities
- Design, develop, and deploy AI/ML models and algorithms
- Design, develop, and deploy AI/ML models and algorithms
- Build predictive models for patient and donor risk assessment (e.g., eligibility, adverse outcomes, fraud/risk scoring)
- Build predictive models for patient and donor risk assessment (e.g., eligibility, adverse outcomes, fraud/risk scoring)
- Work with structured and unstructured healthcare/life sciences data (EHR, clinical, donor data)
- Work with structured and unstructured healthcare/life sciences data (EHR, clinical, donor data)
- Perform data preprocessing, feature engineering, and model evaluation
- Perform data preprocessing, feature engineering, and model evaluation
- Integrate AI models into production systems via APIs or microservices
- Integrate AI models into production systems via APIs or microservices
- Collaborate with data scientists, clinicians, and business stakeholders
- Collaborate with data scientists, clinicians, and business stakeholders
- Optimize model performance, accuracy, and interpretability
- Optimize model performance, accuracy, and interpretability
- Utilize cloud AI/ML platforms (AWS, Azure, Google Cloud)
- Utilize cloud AI/ML platforms (AWS, Azure, Google Cloud)
- Monitor, retrain, and maintain models in production (MLOps practices)
- Monitor, retrain, and maintain models in production (MLOps practices)
- Ensure compliance with healthcare regulations (HIPAA, FDA, GDPR as applicable)
- Ensure compliance with healthcare regulations (HIPAA, FDA, GDPR as applicable)
- Apply ethical AI practices, including bias detection and model transparency
- Apply ethical AI practices, including bias detection and model transparency
- Stay updated with advancements in AI, healthcare analytics, and risk modeling
- Stay updated with advancements in AI, healthcare analytics, and risk modeling
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