Bangalore, Karnataka, India
Information Technology
Full-Time
DIMAAG
Overview
As a Senior/Staff Software Engineer, you’ll design, develop, and optimize back-end services within a distributed microservices ecosystem. You'll have major hands-on development along with architectural decisions, collaborate with cross-functional teams, and mentor engineers to deliver high-impact solutions. You’ll work on core systems that integrate machine learning models, optimize AI pipelines, and deliver real-time insights for software automation platform.
Key Responsibilities
Design & Development
Education:
Key Responsibilities
Design & Development
- Build Scalable Microservices: Architect and develop secure, resilient microservices using Java/Python to handle high-throughput workloads, ensuring compliance with best practices for scalability and fault tolerance.
- Multimedia Stream Processing: Design services to ingest, process, and analyze multimedia streams (images, video) with sub-second latency, leveraging tools like FFmpeg, OpenCV, or GStreamer for real-time operations.
- Cloud-Native Deployment: Deploy and manage services on AWS/GCP/Azure using Docker/Kubernetes, optimizing for cost, performance, and scalability in containerized environments.
- Optimize APIs & Databases: Develop event-driven APIs (REST/gRPC) for seamless integration and optimize SQL/NoSQL databases (PostgreSQL, Cassandra, Redis) for high concurrency and low-latency queries.
- AI/ML Workflow Orchestration: Design and deploy microservices to automate AI/ML pipelines (training, inference, monitoring)
- System Design Leadership: Drive architectural decisions to ensure scalability, security, and resilience across distributed systems, including disaster recovery, load balancing, and data encryption strategies
- Cloud-Native Architecture: Deploy and manage services on AWS/GCP/Azure using Kubernetes (EKS, GKE, AKS) to optimize cost, scalability, and geographic redundancy.
- Containerization & Orchestration: Design Docker/Kubernetes-based workflows for seamless scaling of multimedia processing, AI/ML and other workloads.
- CI/CD Pipelines: Implement robust GitHub Actions/ArgoCD workflows for zero-downtime deployments, automated testing, and rollback strategies.
- Resilient Data Systems: Optimize SQL/NoSQL databases (PostgreSQL, MongoDB) and real-time data layers (Kafka, Redis) for high-throughput multimedia workloads and AI inference results.
- Security & Compliance: Enforce encryption (TLS, AES-256), IAM policies, and audit trails for configuration data across hybrid-cloud environments.
- Edge & Hybrid Deployments: Architect solutions for edge computing to reduce latency in video processing and AI inference.
- Troubleshoot issues, enhance performance, and implement monitoring (Prometheus, Grafana) for system observability.
- Implement distributed tracing using OpenTelemetry for monitoring distributed services.
- Collaboration: Partner with Product, Front-End, DevOps, and QA teams in Agile/Scrum workflows to deliver end-to-end solutions.
- Leadership: Mentor engineers on best practices for real-time systems and establish coding best practices and conduct rigorous code reviews
Education:
- BE/ME in Computer Science or equivalent experience.
- Senior: 5+ years in back-end development with distributed systems; Staff: 8+ years.
- Proven work with distributed systems, microservices, and high-traffic environments.
- Languages: Java, Go, Python, JavaScript/TypeScript.
- Cloud & Tools: AWS/GCP/Azure, Docker, Kubernetes, Kafka/RabbitMQ/Redis.
- Databases: PostgreSQL, MongoDB, Redis.
- DevOps: Kubernetes, Docker, Terraform, CI/CD pipelines (Jenkins, ArgoCD, GitHub Actions).
- Video Tools: FFmpeg, GStreamer, WebRTC, RTSP/RTMP protocols.
- Protocols: REST, gRPC, WebRTC, RTSP, Websockets
- Strong problem-solving, communication, and leadership abilities.
- Strong collaboration skills with cross-functional AI/Product teams.
- Ability to translate complex AI concepts into scalable engineering solutions.
- Contributions to open-source projects.
- Certifications in cloud technologies (AWS/GCP).
- Expertise in performance tuning, security, or advanced system design.
- Experience with edge computing for video processing
- Familiarity with APMs and Cloud monitoring services such as DataDog
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