AI Platform Engineer

BayOne Solutions

10 years

0 Lacs

india

Posted:1 day ago| Platform:

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Skills Required

ai design retrieval model workflow chat code kubernetes data security ui terraform helm monitoring logging reliability devops storage inference compliance engineering architecture software ml programming python orchestration interface routing developer scaling aws gcp governance

Work Mode

On-site

Job Type

Full Time

Job Description

AI Platform Engineer

production readiness

Key Responsibilities

Design, develop, and operate the company’s
AI application platform
, including RAG pipelines, model gateways, MCP integrations, and agent workflows.
Build and maintain secure chat interfaces using
OpenWebUI
or custom UI components.
Own
IaC (Terraform, Helm, etc.)
,
CI/CD automation
, and
Kubernetes
-based deployments for all AI platform components.
Implement robust
observability
solutions (monitoring, logging, tracing) across the platform.
Ensure
platform reliability, uptime, and scalability
using SRE and DevOps best practices.
Optimize
compute, storage, and inference costs
while maintaining performance and quality.
Establish strong
governance
, access control, and compliance processes across AI workloads.
Collaborate cross-functionally with
product, data science, engineering, and security
to deliver high-impact AI features and integrations.
Troubleshoot production issues and continuously improve the platform’s architecture and performance.

Required Skills & Qualifications

4–10+ years of experience in
software engineering, DevOps, or AI/ML platform engineering
.
Strong hands-on programming experience in
Python, Go, or TypeScript
.
Expertise with
Kubernetes
, container orchestration, and cloud-native tooling.
Proficiency with
Terraform
,
Helm
, or other IaC frameworks.
Experience building
RAG pipelines
,
LLM integrations
, or similar AI workflows.
Familiarity with
n8n
,
LangChain
,
OpenWebUI
, or custom chat interface frameworks.
Solid understanding of
observability tools
(Prometheus, Grafana, ELK, OpenTelemetry).
Experience with secure, production-grade deployments of AI or distributed systems.

Preferred Qualifications

Experience with multi-model routing, inference gateways, or vector databases.
Knowledge of MCP (Model Context Protocol) and related developer tooling.
Prior experience scaling AI or distributed systems in cloud environments (AWS, GCP, Azure).
Understanding of SRE principles, access governance, and security best practices.

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