Gen AI Developer

Generative AI Solutions Engineer

Strong production experience delivering Generative AI applications in enterprise or customer-facing environments.

Hands-on expertise with Google Cloud Platform Vertex AI and Gemini models.

Strong experience designing and implementing Retrieval-Augmented Generation (RAG) architectures.

Experience building vector embedding and vector search pipelines.

Strong cloud-native application development experience.

Experience with serverless and container-based cloud solutions.

Strong understanding of AI application architecture and deployment.

Experience with Dev Ops, CI/CD, and MLOps practices.

Strong communication and collaboration skills.

Experience working in Agile software development environments.

8+ years of experience in software development or engineering roles.

Experience implementing enterprise-grade Generative AI applications in production environments.

Hands-on experience with Vertex AI Studio and GCP AI services.

Experience designing data grounding strategies for LLM-based applications.

Experience building pipelines for preparing, processing, and embedding data.

Experience with vector stores such as Vertex AI Vector Search.

Experience with Dev Ops tools such as Git Hub and Azure Dev Ops.

Experience with CI/CD pipelines and MLOps implementation.

Experience with Lang Chain or Agentic AI frameworks preferred.

Experience with Model Context Protocol preferred.

Experience integrating AI solutions with CRM, ERP, eCommerce, EMR, or EHR platforms preferred.

Experience working with Agile methodologies and SDLC processes preferred.

Design, develop, and implement scalable Generative AI solutions on Google Cloud Platform.

Build and maintain Retrieval-Augmented Generation (RAG) systems for enterprise AI applications.

Design and implement vector embedding and vector search pipelines.

Develop data grounding strategies to improve response accuracy and contextual relevance.

Implement AI applications using Vertex AI, Gemini, and related GCP services.

Explore and implement Agentic AI concepts for autonomous AI workflows.

Integrate Model Context Protocol capabilities for enhanced interoperability with external tools and systems.

Collaborate with stakeholders, product owners, developers, and business teams to gather requirements and deliver AI solutions.

Participate in proof-of-concept and proof-of-technology initiatives for new AI capabilities.

Support Agile development practices and collaborate across engineering teams.

Maintain and enhance AI application performance, scalability, and reliability.

Contribute to CI/CD and MLOps processes for AI deployment and monitoring.

Document technical designs, implementation details, and AI workflows.

Adhere to organizational code of conduct and operational standards.

Growth mindset and willingness to learn emerging AI technologies and frameworks.

Strong analytical and problem-solving skills.

Ability to communicate technical concepts clearly to technical and non-technical stakeholders.

Strong collaboration and teamwork abilities.

Ability to work independently in fast-paced enterprise environments.

Strong attention to detail and quality-focused mindset.

Generative AI.

Vertex AI.

Gemini.

Retrieval-Augmented Generation (RAG).

Vector Embeddings.

Vector Search.

Google Cloud Platform (GCP).

Vertex AI Studio.

Lang Chain.

Agentic AI.

Model Context Protocol.

MLOps.

CI/CD.

Git Hub.

Azure Dev Ops.

Cloud-Native Development.

Serverless Architecture.

Container-Based Solutions.

AI Application Development.

Machine Learning Pipelines.

API Integrations.

Agile Methodology.

Dev Ops.

Bachelor’s degree in Computer Science or related field required.

Equivalent combination of education and relevant experience may be considered.