Role: Java Full Stack Developer (AWS, Golang and AI skills)
Location: Ft. Lauderdale, FL/ Phoenix, AZ
Experience: Developer (5-7 years), Sr. Developer (8+ years)
Type: Fulltime permanent role with Infocepts
Role Overview:
We are seeking a highly skilled Full Stack Developer with expertise in Java, AWS, Golang, and AI/ML
integration to build next-gen, intelligent, cloud-native applications. The role focuses on developing
scalable microservices, embedding AI capabilities into business workflows, and delivering high-
performance systems.
Key Responsibilities:
· Design and develop end-to-end solutions across frontend, backend, and AI layers.
· Build scalable microservices using Java (Spring Boot) and Golang for high-performance
· workloads
· Develop modern UI using React/Angular.
· Design and deploy cloud-native applications on AWS
· Integrate AI/ML models and APIs (LLMs, predictive models) into applications
· Build and manage RESTful and event-driven architectures
· Implement CI/CD pipelines and DevOps practices for cloud deployments
· Ensure performance, scalability, and security of applications
· Collaborate with data scientists, architects, and product teams
Required Skills & Qualification:
· 5+ years of full stack development experience
· Strong expertise in Java (5+), Spring Boot, Python, and Golang
· Hands-on experience with AWS services: EC2, Lambda, S3, API Gateway, RDS, DynamoDB,
SNS/SQS, IAM
· Frontend experience with React/Angular, JavaScript, HTML, CSS
· Experience integrating AI/ML services or APIs (OpenAI, AWS Bedrock, SageMaker, etc.)
· Strong knowledge of microservices, REST APIs, and distributed systems
· Experience with Docker, Kubernetes (EKS), and CI/CD tools
· Familiarity and experiences with NoSQL databases like: PostgreSQL, Cassandra, Couchbase,
Redis, Elasticsearch
Preferred Qualifications:
· Experience building AI-powered applications (chatbots, recommendation systems, NLP use
· cases)
· Knowledge of RAG architectures, prompt engineering, and LLM integration
· Experience with streaming/event platforms (Kafka or AWS EventBridge)
· Exposure to MLOps practices and ML lifecycle management
· Understanding of security standards (OAuth2, JWT)