AI Engineer for Infra

NCS Group; Pune District; On-site; INR 2,000,000 - 4,000,000; Full time

Job summary

NCS Group in Pune seeks a proactive AI Engineer with a strong Infrastructure background to design, scale, and optimize high‑performance AI platforms. You will partner closely with Infra Ops to turn operational challenges into automated, business‑oriented AI solutions.

Key responsibilities include building scalable AI infra, ensuring efficient resource use, and implementing robust MLOps practices. Candidates should have 4–8+ years in software/infra with cloud exposure and AI tooling experience.

Qualifications

• 4–8+ years in software or infra engineering with AI/ML infrastructure exposure.
• Cloud architecture experience (AWS/Azure/GCP) and Kubernetes orchestration.
• Experience deploying ML frameworks (PyTorch, TensorFlow) and MLOps tools.
• Proficiency in Python, Go, or C++, and strong Linux skills.

Responsibilities

• Design, scale, and optimize AI platforms with a focus on business value.
• Collaborate with Infra Ops to automate operational pain points via AI workflows.
• Optimize GPU/CPU resources for performance and cost efficiency.
• Implement MLOps practices: CI/CD for ML, monitoring, drift detection, governance.
• Translate business goals into infrastructure initiatives and measure ROI.

Tools

Docker

Kubernetes

Terraform

Ansible

PyTorch

TensorFlow

Kubeflow

MLflow

Ray

Triton Inference Server

Python

Go

C++

Shell scripting

Linux

Job description

We are seeking a proactive AI Engineer with strong Infrastructure background to join our team in Pune. We need someone who looks beyond daily operations to understand how AI infrastructure drives real business value. You will design, scale, and optimize high-performance AI platforms while partnering closely with Infra Ops to turn operational challenges into automated, business-aligned AI solutions.

Key Responsibilities

• AI & Infra Synergy: Design, build, and maintain scalable infrastructure, pipelines, and environments for training and deploying AI/ML models.
• Work hand‑in‑hand with the Infrastructure Operations team to identify operational bottlenecks and solve them using intelligent automation and AI‑driven workflows.
• Optimize resource utilization (GPU/CPU workloads, storage, compute clusters) to balance high performance with cost‑efficiency.
• Business & Value‑Driven Engineering: Translate business goals and operational metrics into concrete AI infrastructure initiatives.
• Measure and report on the ROI of infra improvements (e.g., uptime, latency reduction, cloud spend optimization, developer productivity).
• Drive a product mindset within the operations unit—focusing on long‑term value, system resilience, and scalability rather than quick operational patches.
• MLOps & Platform Reliability: Establish robust MLOps / LLMOps practices (CI/CD for ML, automated monitoring, drift detection, and model governance).
• Implement continuous observability and telemetry for both model performance and underlying infrastructure.

What We’re Looking For

Core Qualifications

• Experience: 4–8+ years of total experience in Software/Infrastructure Engineering, with at least 2–4 years dedicated to AI/ML infrastructure or MLOps.
• Infra Background: Proven track record in cloud architecture (AWS, Azure, or GCP), Kubernetes orchestration, containerization (Docker), and Infrastructure as Code (Terraform, Ansible).
• AI/ML Tooling: Experience with framework deployments (PyTorch, TensorFlow), vector databases, and MLOps tools (Kubeflow, MLflow, Ray, Triton Inference Server).
• Programming: Proficiency in Python, Go, or C++, alongside strong shell scripting and Linux internal skills.

Mindset & Soft Skills

• Business Acumen: Ability to connect technical architecture decisions directly to business outcomes, SLAs, and cost management.
• Problem Solver: Proactive in identifying systemic operational pain points and designing durable AI/automation solutions.
• Collaboration: Strong communication skills with a track record of partnering effectively with cross‑functional teams (DevOps, Data Science, and Business Stakeholders).

Preferred Qualifications

• Hands‑on experience with LLM infrastructure, fine‑tuning setups, or retrieval‑augmented generation (RAG) pipelines at scale.
• Prior experience working in Pune’s tech ecosystem with global distributed teams.
• Certifications in AWS/Azure/GCP Architecture or Kubernetes (CKA/CKAD).