Java Fullstack Engineer with AI

Job Title : AI / ML Engineer Location : Remote Duration : 9 12 Months Job Summary :

We are seeking a highly skilled AI / ML Engineer to join our team for a remote contract engagement. The ideal candidate will have hands-on experience in deploying AI applications on cloud platforms like Google Cloud Platform (GCP) or Microsoft Azure , with exposure to Agentic AI frameworks such as LangChain, LangGraph, or ADK. The role involves designing, developing, and scaling AI applications on containerized platforms like Kubernetes , while ensuring robust security and performance.

Key Responsibilities :

Lead AI application development and production deployment on GCP / Azure .

Develop Agentic AI solutions using frameworks like LangChain, LangGraph, or ADK .

Architect, implement, and scale AI applications on Kubernetes clusters.

Write efficient, production-ready code in Python and Java for AI / ML pipelines.

Collaborate with cross-functional teams on MCP, A2A integrations, and agent collaborations .

Implement security best practices in AI applications (e.g., Authentication, TLS, Token validation).

Optimize and troubleshoot AI pipelines for performance, scalability, and reliability.

Contribute to platform engineering for AI applications, ensuring modular and maintainable solutions.

Keep up-to-date with emerging AI technologies and frameworks.

Required Skills & Qualifications :

Strong programming expertise in Python and Java .

Hands-on experience with AI / ML production deployments on GCP or Azure .

Knowledge of Agentic AI frameworks (LangChain, LangGraph, ADK).

Experience with containerization and orchestration (Docker, Kubernetes).

Familiarity with AI application security constructs : Auth mechanisms, TLS, token validations.

Exposure to MCP, A2A integrations, and multi-agent collaboration workflows .

Strong problem-solving skills and ability to work in a remote team environment .

Familiarity with scalable AI pipelines and cloud-based AI services .

Preferred Skills :

Experience in platform engineering for AI applications .

Knowledge of agent collaboration strategies in AI applications.

Understanding of cloud-native application design .

Familiarity with CI / CD pipelines for AI / ML deployments.

Exposure to performance optimization and monitoring in cloud-based AI systems.