Lead DevOps AI Engineer

Trine Infotech; India; Remote; INR 2,500,000 - 6,000,000; Full time

Job summary

Trine Infotech is seeking a Lead DevOps / AI Engineer to join our SRE team. You will design scalable cloud infrastructure, lead automation and ML deployment initiatives, and drive reliability across production environments.

You will own IaC, CI/CD and observability, work with AWS, Kubernetes, Docker, and MLOps pipelines, and mentor engineers on best practices for secure, cost-efficient operations. The role demands 8+ years in DevOps/SRE or cloud platforms, strong scripting in Python/Go, and

Qualifications

• 8+ years in DevOps, SRE or cloud platform engineering.
• Hands-on experience with AWS, Kubernetes and Docker.
• Proficiency in Terraform, Pulumi or CloudFormation.
• Strong scripting in Python, Go or Bash.
• Experience with GitHub Actions, GitLab CI or ArgoCD.
• Hands-on with Prometheus, Grafana, Datadog or OpenTelemetry.
• Exposure to AI/ML deployment pipelines.

Responsibilities

• Design, build and manage scalable cloud infrastructure with IaC.
• Manage AWS, Kubernetes and Docker environments.
• Build CI/CD and ML pipelines for models and LLMs.
• Implement AI-driven anomaly detection and automated incident response.
• Lead DevOps, SRE, automation and MLOps decisions.

Skills

DevOps & SRE leadership

AWS & Cloud Platforms

Kubernetes & Docker

IaC: Terraform, Pulumi, CF

Scripting: Python/Go/Bash

CI/CD: GitHub Actions/GitLab CI/ArgoCD

Observability: Prometheus/Grafana/Data

AI/ML deployment pipelines

Tools

Terraform

Pulumi

CloudFormation

Docker

Kubernetes

GitHub Actions

GitLab CI

ArgoCD

Prometheus

Grafana

Datadog

OpenTelemetry

LangChain

Job description

We are looking for a Lead DevOps / AI Engineer to join our Site Reliability Engineering (SRE) team. The role combines DevOps, cloud infrastructure, automation, AIOps and MLOps to build highly reliable and scalable production environments.

Key Responsibilities:

• Design, build and manage scalable cloud infrastructure using Infrastructure as Code.
• Manage and optimize AWS, Kubernetes and Docker environments.
• Build and maintain CI/CD and MLOps pipelines for ML models and LLMs.
• Implement AI-driven anomaly detection, predictive alerting and automated incident response.
• Develop automation and self-healing workflows to improve system reliability and reduce MTTR.
• Manage observability using Datadog, Prometheus, Grafana and OpenTelemetry.
• Optimize AI/ML workloads, including GPU/CPU utilization, inference performance and cloud costs.
• Implement security, disaster recovery and reliability best practices.
• Lead technical decisions and provide guidance across DevOps, SRE, automation and MLOps.

Required Skills:

• 8+ years of experience in DevOps, SRE or Cloud Platform Engineering.
• Strong hands‑on experience with AWS, Kubernetes and Docker.
• Proficiency in Terraform, Pulumi or CloudFormation.
• Strong scripting/programming skills in Python, Go or Bash.
• Experience with GitHub Actions, GitLab CI or ArgoCD.
• Hands‑on experience with Prometheus, Grafana, Datadog or OpenTelemetry.
• Hands‑on exposure to AI/ML deployment pipelines.

Good to Have:

• Experience with MLflow, Kubeflow, Ray, vLLM or Triton Inference Server.
• Experience deploying LLMs or Generative AI solutions in production.
• Knowledge of vector databases such as Pinecone, Milvus or Qdrant.
• Familiarity with LangChain or LlamaIndex.