Agentic AI Engineer

About the role

Title: Agentic AI Engineer

Location: Remote (PDT working hours)

About the Role:

We are seeking a highly skilled Machine Learning Engineer / AI Engineer to design and build next-generation AI-powered business solutions. This is not a traditional Data Scientist role. The ideal candidate will have a strong Software Engineering foundation combined with hands-on experience developing production-ready AI applications using modern Generative AI and Agentic AI technologies.

You will work on intelligent automation initiatives, AI agents, and enterprise AI solutions that solve real business problems across areas such as operational intelligence, forecasting, inventory optimization, pricing, and workflow automation. The ideal consultant should be capable of contributing immediately with minimal ramp-up and demonstrate strong ownership throughout the engagement.

Key Responsibilities:

Design, develop, and deploy production-ready AI applications using modern Generative AI technologies.

Build Agentic AI workflows and intelligent AI agents capable of autonomous reasoning and task execution.

Develop Retrieval-Augmented Generation (RAG) solutions leveraging vector databases and memory architectures.

Integrate Large Language Models (LLMs) with enterprise systems and APIs.

Build scalable cloud-native AI solutions on Google Cloud Platform (GCP).

Develop and optimize data pipelines using BigQuery and relational databases.

Deploy and manage applications using Docker, Kubernetes (GKE), and modern CI/CD practices.

Collaborate with engineering teams to deliver secure, scalable, and maintainable AI solutions.

Troubleshoot, optimize, and continuously improve AI applications in production environments.

Contribute to MLOps and AI deployment best practices.

Required Qualifications:

6–10 years of overall software engineering or AI engineering experience (exceptional candidates with relevant expertise will also be considered).

Strong hands-on experience with Google Cloud Platform (GCP).

Advanced proficiency in Python.

Experience building enterprise AI applications using Large Language Models (LLMs).

Hands-on experience with Agentic AI, AI agents, intelligent automation, or autonomous workflows.

Strong knowledge of Retrieval-Augmented Generation (RAG) architectures.

Experience working with vector databases and memory frameworks.

Experience with BigQuery and relational databases such as PostgreSQL.

Familiarity with AI SDKs and API integration.

Experience with Docker, Google Kubernetes Engine (GKE), and CI/CD pipelines.

Exposure to MLOps concepts and deployment practices.

Strong software engineering fundamentals, problem-solving abilities, and debugging skills.

Excellent communication skills with the ability to explain technical solutions and business impact.