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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 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 unitfocusing 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 Were Looking For Core Qualifications Experience: 48+ years of total experience in Software/Infrastructure Engineering, with at least 24 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 Punes tech ecosystem with global distributed teams. Certifications in AWS/Azure/GCP Architecture or Kubernetes (CKA/CKAD). .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.