GenAI Technical Architect

Prodapt Solutions

10 - 15 years

30 - 45 Lacs

chennai

Posted:20 hours ago| Platform:

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Skills Required

continuous integration python ci/cd docker debugging kubernetes github css technical leadership glm cloud platform javascript microservices sql spring react.js technical architecture java gcp design patterns datadog splunk code review typescript

Work Mode

Work from Office

Job Type

Full Time

Job Description

Overview

Prodapt is looking for a strong

backend and platform-focused engineer

to join the AI Gateway team. This individual will play a key role in the

end-to-end design, development, and deployment

of GenAI services built on Google Cloud Platform (GCP), leveraging

cloud-native architecture and scalable infrastructure

This is a high-impact engineering role suited for someone with a strong backend foundation, platform thinking, and a passion for building robust, production-grade GenAI applications.

Responsibilities

Lead and contribute to the
design, development, and deployment
of GenAI applications using
Kubernetes (GKE)
and modern cloud-native patterns.
Build and maintain
CI/CD pipelines
using tools like
Argo CD
,
GitHub Actions
, and infrastructure-as-code practices.
Collaborate with ML engineers, infra teams, and product stakeholders to deliver
secure, scalable, and observable services
.
Write clean, maintainable, and efficient code primarily in
Python
and other backend languages.
Support
front-end development
and integration efforts where needed to ensure smooth end-to-end delivery.
Provide
technical leadership
to a smaller group or squad through code reviews, mentorship, and architecture guidance.
Monitor and optimize application health and performance using tools like
Splunk
and
Datadog
.

What We're Looking For:

Strong proficiency in
cloud-native architecture and scaling solutions
using
GCP and GKE
.
Experience with
CI/CD pipelines
, ideally with
Argo CD
, GitHub Actions, and containerized deployments (Docker, Kubernetes).
Proficient in
Python
and familiar with backend frameworks and patterns.
Familiarity with
observability and monitoring tools
like
Splunk, Datadog
, or equivalents.
Exposure to
full-stack development
(e.g., React, TypeScript) is a plus.
Strong analytical, debugging, and
problem-solving skills
.
Excellent
communication and collaboration
abilities; ability to work across cross-functional teams.

Bonus Points:

Experience working with
LLMs or Generative AI frameworks
(e.g., LangChain, LlamaIndex).
Prior contributions to
AI infrastructure, MLOps
, or large-scale microservices.
Proven ability to
lead or mentor
junior developers and influence engineering practices.

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