DevOps Engineer I – AWS & AI
- Location: Pune, India (Hybrid – Onsite)
- Employment Type: Full-Time
- Experience Level: 3–5 Years
- Notice Period: Immediate Joiners Preferred
About the Opportunity We are hiring for a fast-growing product-based company based in Pune, looking for a skilled DevOps Engineer I to design, build, and scale cloud infrastructure on Amazon Web Services (AWS). This role carries a strong focus on AI workloads, LLMOps, and hands-on involvement in high-impact AI-driven product initiatives. Candidates with demonstrated experience working in product-based companies and delivering AI projects at scale are strongly preferred.
Role Overview You will be embedded in a cross-functional engineering team responsible for building, maintaining, and scaling infrastructure that supports both production workloads and internal AI/ML systems. This role requires strong ownership, sound architectural judgment, and a relentless focus on reliability, security, and cost efficiency across AWS-native services.
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
- 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
- Experience: 3–5 years of hands-on experience in DevOps, SRE, or cloud infrastructure roles within a product-based company
- Cloud Infrastructure: Strong hands-on expertise with core AWS services: EKS, ECS/Fargate, EC2, S3, RDS, SageMaker, Lambda, VPC, and CloudFront
- AI & LLM Systems: Experience deploying and managing 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)
- Systems & Networking: Strong understanding of Linux/Unix systems (preferably Ubuntu), networking fundamentals, security architecture, and AWS IAM principles
- Production Deployment: 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 (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
Soft Skills
- Excellent verbal and written communication skills with a habit of thorough documentation
- Strong analytical thinking and structured problem-solving approach
- Quick learner with the ability to adapt to evolving AI tooling and cloud-native ecosystems
- Collaborative team player with the ability to mentor peers and contribute to engineering culture
Interested candidates are encouraged to share their updated resume along with their availability for an immediate discussion.
