DevOps Engineer I – AWS

Job Overview

• Role: DevOps Engineer I – AWS & AI
• Experience: 3–5 Years (Product-based company experience strongly preferred)
• Notice Period: Immediate Joiners Preferred
• Location: Pune, India (Onsite)
• Employment Type: Full-Time

About the Opportunity & Role Overview

We are hiring for a fast-growing product-based company in Pune looking for a skilled DevOps Engineer I to design, build, and scale cloud infrastructure on AWS. This role carries a strong focus on AI workloads, LLMOps, and hands-on involvement in high-impact AI-driven product initiatives. You will be embedded in a cross-functional engineering team responsible for maintaining and scaling infrastructure for production workloads and internal AI/ML systems, with a focus on reliability, security, and cost efficiency.

Key Responsibilities

• AWS Infrastructure & IaC: Own and maintain production AWS infrastructure using Terraform and Ansible to ensure high availability, fault tolerance, and proactive monitoring.
• AI & LLM Deployment: Deploy and manage AI/LLM workloads, vector databases, and model inference pipelines.
• CI/CD Pipelines: Design and maintain secure CI/CD pipelines for microservices and AI/ML systems.
• Security & Compliance: Implement security best practices, including IAM policies, secrets management, and vulnerability scanning.
• Observability: Set up observability and monitoring for system and AI metrics (latency, throughput, cost, usage).
• Cost Optimization: Optimize cloud costs, particularly for GPU-backed and compute-intensive AI workloads.
• Networking & Traffic: Manage VPC networking, DNS, load balancing, and secure connectivity across AWS environments.
• Cross-Functional Collaboration: Partner with AI/ML engineers and product teams to support fast experimentation and reliable production deployments.

Required Qualifications

• Experience: 3–5 years of hands-on experience in DevOps, SRE, or cloud infrastructure roles within a product-based company.
• AWS Core Services: Strong expertise with EKS, ECS/Fargate, EC2, S3, RDS, SageMaker, Lambda, VPC, and CloudFront.
• AI Infrastructure: Production experience deploying AI/LLM workloads, vector databases (e.g., Pinecone, Weaviate, pgvector), and LLM APIs (OpenAI, Bedrock, etc.).
• Containerization & IaC: Solid expertise in Docker, Kubernetes (EKS), Terraform, and Ansible.
• Automation: Proficiency in Python scripting for automation, tooling, and infrastructure workflows (Mandatory).
• OS & Networking: Strong understanding of Linux/Unix systems (preferably Ubuntu), networking fundamentals, and security architecture/IAM on AWS.

Preferred Skills

• LLMOps & ML Tools: Experience with LLMOps practices (prompt versioning, model monitoring, RAG pipelines, inference optimization) and AI/ML frameworks (SageMaker Pipelines, MLflow, Kubeflow).
• Managed AI Services: Exposure to AWS Bedrock, Rekognition, Comprehend, or other managed AI services.
• Observability Tools: Hands-on experience with Prometheus, Grafana, CloudWatch, or OpenTelemetry.
• Troubleshooting: Strong debugging, performance tuning, and root-cause analysis skills across distributed systems.

Soft Skills

• Communication & Documentation: Excellent verbal and written communication with a habit of thorough documentation.
• Problem-Solving & Learning: Strong analytical thinking, structured problem-solving, and the ability to quickly adapt to evolving AI tooling.
• Teamwork: Collaborative team player ready to mentor peers and contribute to engineering culture.