Staff Software Engineer

Job Summary As a 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 - Build Scalable Microservices: Architect and develop secure, resilient microservices using 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 Scalable Infrastructure - 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. Observability & Performance: - Troubleshoot issues, enhance performance, and implement monitoring (Prometheus, Grafana) for system observability. - Implement distributed tracing using Open Telemetry for monitoring distributed services. Collaboration & Leadership: - 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 Qualifications Education: - BE/MTech in Computer Science or equivalent branch. Experience: - 8+ years of relevant experience - Proven work with distributed systems, microservices, and high-traffic environments Technical Skills: - Languages: Python, Go, 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 Soft Skills: - 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. Preferred Qualifications - 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