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)