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