AI Engineer for Infra

Position - AI Engineer for Infra

About the Role

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

1. 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.

1. 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.

1. 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).