Software Engineer

Job Title: Software Engineer AI ML/Data science development

Location: Bangalore

About the Role

We are seeking a highly skilled and motivated AI Engineer to join our innovative team at B-MEC IMAGING, dedicated to advancing healthcare through technology. The successful candidate will be responsible for designing, building, and deploying scalable Artificial Intelligence and Machine Learning models that directly impact patient care, diagnostics, and clinical efficiency. This is a critical, high-stakes role that demands expertise in the full ML lifecycle alongside a deep commitment to data privacy, clinical safety, and regulatory compliance (e.g., HIPAA, FDA, DEKRA). You will transform complex clinical data into actionable intelligence, working collaboratively with clinicians and data scientists to bring validated, reliable AI solutions from research to the patient bedside.

Key Responsibilities

• Design and Development:• Design, develop, and implement robust and scalable Machine Learning (ML) models and deep learning algorithms to solve complex clinical problemsby helping in bringing, clinical workflow efficiency, enabling smart hospital solutions and AI assisted decisions support. • Work with large, complex datasets, DICOM images, ensuring data integrity and preparing it for modelling.• Design, develop, and implement highly accurate and robust Machine Learning (ML) and Deep Learning models for specific medical applications (e.g., diagnostic imaging analysis, predictive models for patient risk, natural language processing for clinical notes).• Focus on developing algorithms that offer high sensitivity and specificity relevant to clinical needs.

• Data Handling and Preprocessing:

• Work extensively with specialized medical data types, including DICOM images (MRI, CT scans, X-rays), EHR (Electronic Health Record) data, genomic sequences, and time-series physiological signals.• Ensure rigorous data cleaning, normalization, and feature extraction from complex, often noisy, clinical datasets.

• Safety, Reliability, and Explainability:

• Develop models with inherent mechanisms for safety checks and failure modes appropriate for clinical use.• Prioritize Model Explainability (XAI) to provide clinically relevant interpretations of model predictions, ensuring transparency for physicians (e.g., using techniques like SHAP or LIME).

• Compliance and Regulatory Standards:

• Integrate development practices that adhere strictly to medical regulatory standards, such as HIPAA/GDPR for patient data privacy and potentially FDA/CE Mark requirements for medical device software.• Maintain detailed documentation of model validation, performance metrics, and data lineage to support regulatory submissions.

• Benchmarking and Clinical Validation:

• Rigorously benchmark model performance against established clinical standards or competing algorithms.• Collaborate with clinicians and biostatisticians to design and execute clinical validation studies to demonstrate real-world utility and impact.

Deployment and MLOps

• Production System Integration:o Integrate validated models into clinical workflow systems, such as EHR platforms or PACS (Picture Archiving and Communication Systems), ensuring seamless, low-latency performance in a hospital or diagnostic setting.o Integrate ML models into production applications and systems, working closely with software development teams.o Establish and maintain MLOps practices, including version control, continuous integration/continuous deployment (CI/CD) for ML pipelines, and model monitoring.

• Safety-Critical MLOps Pipelines: (Good to have)o Design and implement robust MLOps pipelines that incorporate extra validation and approval gates specific to healthcare. This includes mandatory clinical review steps before model updates are pushed to production.o Utilize version control for models, training data, and environment configurations to ensure full auditability and traceability, critical for regulatory compliance.• Continuous Monitoring and Validation: (Good to have)o Establish continuous monitoring systems that track model performance against clinical endpoints (e.g., false-negative rate for a diagnostic tool) rather than just technical metrics (e.g., AUC).o Implement data drift detection and concept drift monitoring tailored to changes in patient populations, equipment updates, or diagnostic protocols, triggering alerts and automated retraining processes when performance degrades.• Optimization and Maintenance:o Optimize models for performance, scalability, and efficiency (e.g., latency, throughput).o Monitor deployed models for drift, bias, and performance degradation, and retrain models, as necessary.

• Security and Privacy:o Maintain strict adherence to data governance policies, implementing de-identification or pseudonymization techniques for sensitive patient data both during training and inference.o Ensure all data access and model endpoints are secured using industry-standard encryption and access controls to maintain HIPAA/GDPR compliance.

Required Qualifications and Skills

• Core Experience:o Minimum of 3-4 years of experience developing and deploying AI/ML models, with at least 2 years focused on healthcare, medical imaging, or bioinformatics.o Strong proficiency in Python, including deep familiarity with specialized libraries for medical data handling (e.g., pydicom, SimpleITK).• Deep Learning & Data Types:o Proven experience working with Convolutional Neural Networks (CNNs), for image analysis (e.g., tumoursegmentation, lesion detection) or Recurrent Neural Networks (RNNs)/Transformers for clinical text and time-series data, Natural language processing (NLP) for human interfacing. o Demonstrated ability to handle large, multi-modal datasets common in healthcare, including medical images (DICOM) and structured EHR data.• Compliance and Regulation Knowledge:o Working knowledge of health data regulations (e.g., HIPAA, GDPR) and an understanding of the regulatory landscape for AI in medicine (e.g., FDA guidance on SaMD - Software as a Medical Device).• Collaboration & Communication:o Experience collaborating directly with clinicians, radiologists, or pathologists to gather requirements and validate model performance in a clinical context.o Excellent communication skills, capable of explaining complex ML concepts to non-technical stakeholders (e.g., doctors, hospital administrators).• Research and Collaboration:• Stay up to date with the latest advancements in AI/ML research and apply relevant techniques.• Collaborate with data scientists, product managers, and other engineering teams to understand requirements and deliver end-to-end solutions.

Required Qualifications and Skills

• Education: Bachelor’s or master’s degree in computer science, Data Science, Electrical Engineering, or a related quantitative field.• Programming & Frameworks:• Strong proficiency in Python and its associated data science ecosystem (NumPy, Pandas, Scikit-learn).• Experience with deep learning frameworks like TensorFlow, PyTorch, or Keras.• ML & Data:• Solid understanding of machine learning principles, statistical analysis, and algorithm design.• Experience with data preprocessing, feature engineering, and model evaluation techniques.• Familiarity with database technologies (SQL/NoSQL) and experience working with large data stores.• Cloud & Deployment:• Hands-on experience with cloud platforms (e.g., AWS, Google Cloud Platform, Azure) and their ML services.• Familiarity with containerization technologies like Docker and orchestration tools like Kubernetes is a plus.• Experience deploying models via REST APIs or similar services.• Agile methodology:• Knowledge on Scrum Agile methodology is preferred

Preferred Qualifications (Nice to Have)

• Experience with specific AI domains such as Natural Language Processing (NLP), Computer Vision, or Reinforcement Learning.• Knowledge of Big Data technologies (e.g., Spark, Hadoop).• Experience with ML experiment tracking tools (e.g., MLflow, Weights & Biases).• Prior experience in a production MLOps environment.

Job Types: Full-time, Permanent

Pay: ₹429,593.20 - ₹1,743,765.89 per year

Work Location: In person