DevOps Engineer I – AWS & AI

Pune, Fulltime, Hybrid

Key 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 deployment

Required 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

• Strong 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 environments

Preferred 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 skills

DevOps Engineer I AWS & AI