Specialist - IT - Engineering

About the Role

We're looking for a Backend Engineer who has built and run production systems at scale. You'll own services end-to-end: design, build, deploy, monitor and be able to address prod issues on priority. We also want someone who treats AI-assisted development as a core part of the modern engineering, and who has a track record of stepping up as a technical leader. What Is Expected: Design, build, and operate distributed microservices running on Kubernetes in AWS Own services through their full lifecycle: architecture, coding, testing, deployment, on-call, and iteration based on production feedback Design and evolve event-driven pipelines using Kafka (or similar streaming systems) Work across the AWS stack (EC2, RDS, S3, IAM, VPC, Lambda, ECS/EKS, CloudWatch, etc.) and make informed trade-offs on cost, scalability, and reliability Use AI coding assistants and internal AI tooling to accelerate development, review, and debugging - and help the team adopt them effectively Identify opportunities to apply AI/ML capabilities (e.g., LLM-based features, intelligent automation, agentic workflows) where they solve real product or engineering problems Drive technical decisions on your team: propose designs, run design reviews, and push back constructively when needed Mentor junior and mid-level engineers through code reviews, pairing, and design discussions Take ownership of incidents - from detection through root-cause analysis and postmortems - and drive follow-through on action items Partner with product, SRE/DevOps, and other engineering teams to plan and deliver roadmap work Demonstrated ownership: Has driven a project or service from design through production and long-term maintenance Has taken point on a production incident - led triage, communicated status, and driven the postmortem/fix

Required Qualifications

Core Engineering 5+ years of professional backend software engineering experience Strong experience designing and operating microservices architectures in production Hands-on experience with Kubernetes (deployments, services, config/secrets management, autoscaling, load balancing, debugging pods/nodes in production) Proficiency with Apache Kafka or equivalent (event streaming, partitioning, consumer groups, schema management) Strong working knowledge of AWS - RDS, EC2, S3, IAM, VPC/networking basics, and at least one compute/orchestration service (ECS, EKS, or Lambda) Solid understanding of relational databases (schema design, indexing, query optimization, migrations) — RDS/Postgres or MySQL preferred Proficiency in at least one backend language commonly used (e.g., Java, Go, Python, Node.js) Understanding of API design (REST and/or gRPC), authentication/authorization patterns, and API versioning Practical experience with observability — structured logging, metrics, and distributed tracing (e.g., CloudWatch, Prometheus/Grafana, Datadog, Open Telemetry) Comfort with asynchronous, event-driven, and eventually-consistent system design Basic understanding of CI/CD pipelines and Infrastructure as Code (Terraform, CloudFormation, or similar) Security fundamentals: secrets management, least-privilege IAM, dependency/vulnerability scanning and remediation AI Fluency Regular hands-on use of AI coding assistants (e.g., Kiro, GitHub Copilot, Claude Code, Cursor) in real development workflows Working understanding of core AI/LLM concepts: prompting, embeddings, RAG, tool use/function calling, and agentic workflows — enough to evaluate where they add value and where they don't Judgment about AI tool limitations and knows when to trust AI-generated code vs. when to verify manually, especially around security, correctness, and data handling Nice to Have Experience in migrating workloads to Cloud, DB migrations etc Experience with service mesh (Istio/Gloo), API gateways, or multi-region/multi-AZ architectures Exposure to cost optimization / FinOps practices in AWS Experience with NoSQL stores (DynamoDB, Redis, Elasticsearch/OpenSearch) alongside RDS Experience building or integrating with LLM-based features in production Bangalore Karnātaka India