We are seeking a proactive AI engineer with a strong infrastructure background to join our team in Pune. You will design, scale, and optimize high-performance AI platforms, partnering closely with Infrastructure Operations to turn operational challenges into automated, business-aligned AI solutions.
Key Responsibilities
AI and Infrastructure
Design, build, and maintain scalable infrastructure, pipelines, and environments for training and deploying AI/ML models.
Collaborate with Infrastructure Operations to identify operational bottlenecks and address them with intelligent automation and AI-driven workflows.
Optimize GPU and CPU workloads, storage, and compute clusters to balance performance and cost efficiency.
Business and Value-Driven Engineering
Translate business goals and operational metrics into concrete AI infrastructure initiatives.
Measure and report the return on investment of infrastructure improvements, such as uptime, latency reduction, cloud spend optimization, and developer productivity.
Promote a product mindset within the operations unit, focusing on long-term value, system resilience, and scalability.
MLOps and Platform Reliability
Establish robust MLOps/LLMOps practices, including CI/CD for ML, automated monitoring, drift detection, and model governance.
Implement continuous observability and telemetry for model performance and underlying infrastructure.
What We’re Looking For
Core Qualifications
Experience: 12+ years in software or infrastructure engineering, including at least 2 years dedicated to AI/ML infrastructure or MLOps.
Infrastructure: Proven experience with cloud architecture (AWS, Azure, or GCP), Kubernetes, Docker, and Infrastructure as Code tools such as Terraform or Ansible.
AI/ML Tooling: Experience deploying frameworks such as PyTorch and TensorFlow, and working with vector databases and MLOps tools such as Kubeflow, MLflow, Ray, or Triton Inference Server.
Programming: Proficiency in Python, Go, or C++, along with strong shell scripting and Linux skills.
Mindset and Soft Skills
Business Acumen: Ability to connect technical architecture decisions to business outcomes, SLAs, and cost management.
Problem Solving: Proactive approach to identifying systemic operational challenges and designing durable AI and automation solutions.
Collaboration: Strong communication skills and experience partnering with cross-functional teams, including DevOps, Data Science, and business stakeholders.
Mandatory Skills
AI/LLM integration for IT operations.
Deep hands-on experience with AWS or Azure, including:
Cloud architecture design and implementation.
Networking, IAM, and security best practices.
Infrastructure as Code, including Terraform.
Automation scripting with Python or Go.
Observability and monitoring solutions.
ITSM integration.
Practical experience with AI/LLM APIs or Retrieval-Augmented Generation (RAG).
Preferred Qualifications
Hands-on experience with LLM infrastructure, fine-tuning setups, or RAG pipelines at scale.
Experience working with global distributed teams in Pune’s tech ecosystem.
Certifications in AWS, Azure, or GCP architecture, or Kubernetes (CKA/CKAD).
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