Sr. Engineer - AIML Backend

Responsibilities- Develop and maintain backend microservices using Python, Java and Spring Boot- Build and integrate APIs (both GraphQL and REST) for scalable service communication- Deploy and manage services on Google Cloud Platform (GKE)- Work with Google Cloud Spanner (Postgres dialect) and pub/sub tools like Confluent Kafka (or similar)- Automate CI/CD pipelines using GitHub Actions and Argo CD- Design and implement AI-driven microservices- Collaborate with Data Scientists and MLOps teams to integrate ML Models- Implement NLP pipelines - Enable continuous learning and model retraining workflows using Vertex AI or Kubeflow on GCP- Enable observability and reliability of AI decisions by logging model predictions, confidence scores and fallbacks into data lakes or monitoring tools Required Qualifications- 5+ years of backend development experience with Java and Spring Boot- 2+ years working with APIs (GraphQL and REST) in microservices architectures- 2+ years' experience integrating or consuming ML/AI models in production environments (, TensorFlow Serving or Vertex AI Endpoints) - Experience working with structured and unstructured data (, clinical documents, NLP processing). - Familiarity with ML model lifecycle - from data ingestion, training, deployment, to real-time inference (MLOPS) - 2+ years hands-on experience with GCP, AWS, or Azure- 2+ years working with pub/sub tools like Kafka or similar- 2+ years' experience with databases (Postgres or similar)- 2+ years' experience with CI/CD tools (GitHub Actions, Jenkins, Argo CD, or similar) Preferred Qualifications- Hands-on experience with Google Cloud Platform- Familiarity with Kubernetes concepts; experience deploying services on GKE is a plus- Strong understanding of microservice best practices and distributed systems- Familiarity with Vertex AI, Kubeflow or similar AI platforms on GCP for model training and serving - Understanding of GenAI use cases, LLM prompt engineering and agentic orchestration (, transformers) - Experience deploying Python-based ML Services into Java microservice ecosystems (via REST, gRPC or sidecar patterns) - Knowledge of claim adjudication, Rx domain logic or healthcare specific workflow automation Education Bachelor's degree or equivalent experience (High School Diploma and 4 years relevant experience) Responsibilities- Develop and maintain backend microservices using Python, Java and Spring Boot- Build and integrate APIs (both GraphQL and REST) for scalable service communication- Deploy and manage services on Google Cloud Platform (GKE)- Work with Google Cloud Spanner (Postgres dialect) and pub/sub tools like Confluent Kafka (or similar)- Automate CI/CD pipelines using GitHub Actions and Argo CD- Design and implement AI-driven microservices- Collaborate with Data Scientists and MLOps teams to integrate ML Models- Implement NLP pipelines - Enable continuous learning and model retraining workflows using Vertex AI or Kubeflow on GCP- Enable observability and reliability of AI decisions by logging model predictions, confidence scores and fallbacks into data lakes or monitoring tools Required Qualifications- 5+ years of backend development experience with Java and Spring Boot- 2+ years working with APIs (GraphQL and REST) in microservices architectures- 2+ years' experience integrating or consuming ML/AI models in production environments (, TensorFlow Serving or Vertex AI Endpoints) - Experience working with structured and unstructured data (, clinical documents, NLP processing). - Familiarity with ML model lifecycle - from data ingestion, training, deployment, to real-time inference (MLOPS) - 2+ years hands-on experience with GCP, AWS, or Azure- 2+ years working with pub/sub tools like Kafka or similar- 2+ years' experience with databases (Postgres or similar)- 2+ years' experience with CI/CD tools (GitHub Actions, Jenkins, Argo CD, or similar) Preferred Qualifications- Hands-on experience with Google Cloud Platform- Familiarity with Kubernetes concepts; experience deploying services on GKE is a plus- Strong understanding of microservice best practices and distributed systems- Familiarity with Vertex AI, Kubeflow or similar AI platforms on GCP for model training and serving - Understanding of GenAI use cases, LLM prompt engineering and agentic orchestration (, transformers) - Experience deploying Python-based ML Services into Java microservice ecosystems (via REST, gRPC or sidecar patterns) - Knowledge of claim adjudication, Rx domain logic or healthcare specific workflow automation Education Bachelor's degree or equivalent experience (High School Diploma and 4 years relevant experience)