Job Type: Full-time

Remote: Hybrid

DevOps Engineer I – AWS & AIPune, Fulltime, HybridKey Responsibilities• Own and maintain production AWS infrastructure with high availability, fault tolerance, and proactive monitoring• Build and manage scalable cloud infrastructure using Terraform and Ansible on AWS• Deploy and manage AI/LLM workloads, vector databases, and model inference pipelines• Design and maintain secure CI/CD pipelines for microservices and AI/ML systems• Implement security best practices including IAM policies, secrets management, and vulnerability scanning DevOps Engineer I – AWS & AI• Set up observability and monitoring for both system and AI metrics (latency, throughput, cost, usage)• Optimize cloud costs, particularly for GPU-backed and compute-intensive AI workloads• Manage VPC networking, DNS, load balancing, and secure connectivity across AWS environments• Collaborate closely with AI/ML engineers and product teams to ensure infrastructure supports fast experimentation and reliable deploymentRequired Qualifications• 3 - 5 years of hands-on experience in DevOps, SRE, or cloud infrastructure roles• Proven experience working at a product-based company with fast-paced engineering teams• Strong hands-on expertise with core AWS services: EKS, ECS/Fargate, EC2, S3, RDS, SageMaker, Lambda, VPC,



and CloudFront• Experience deploying and managing AI/LLM workloads, vector databases (e.g., Pinecone, Weaviate, pgvector), and LLM APIs (OpenAI, Bedrock, etc.)• Solid expertise in Docker, Kubernetes (EKS), Terraform, and Ansible• Proficiency in Python scripting for automation, tooling, and infrastructure workflows — this is a mandatory requirement• Robust understanding of Linux/Unix systems, preferably Ubuntu • Sound knowledge of networking fundamentals, security architecture, and IAM principles on AWS• Demonstrated experience delivering AI projects in production — not just experimental or PoC environmentsPreferred Skills• Experience with LLMOps practices — prompt versioning, model monitoring, RAG pipelines, and inference optimization• Familiarity with AI/ML frameworks (SageMaker Pipelines, MLflow, Kubeflow) and model serving patterns• Hands-on experience with observability tools such as Prometheus, Grafana, CloudWatch, or OpenTelemetry• Automation-first mindset with the ability to work across complex distributed systems • Exposure to AWS Bedrock, Rekognition, Comprehend, or other managed AI/ML services• Strong debugging, performance tuning, and root cause analysis skillsSeniority LevelMid-Senior levelIndustryIT Services and IT ConsultingEmployment TypeFull-time

📌 DevOps Engineer I – AWS & AI (India)
🏢 Panzer Technologies
📍 India