Software Architecture

Description

Key Responsibilities

• Design and own the end-to-end cloud architecture on AWS — from edge device connectivity through data processing to ML model serving
• Architect IoT data pipelines for high-throughput ingestion, processing, and storage of sensor and telemetry data from fleet devices
• Design big data infrastructure for batch and real-time analytics at scale — data lake architecture, ETL/ELT workflows, and query optimization
• Build and operationalize ML pipelines — model training infrastructure, feature stores, experiment tracking, and model deployment/serving
• Define the system's data strategy — schema design, data governance, lineage, partitioning, and lifecycle management
• Establish architectural standards — API design, service boundaries, event-driven patterns, and infrastructure-as-code practices
• Ensure security, compliance, and cost optimization are embedded into every architectural decision

Requirements

Must Have

• 10+ years of software engineering experience, with 5+ years in a software architect or principal engineer role
• Proven track record of designing and building large-scale systems from scratch — not just extending existing platforms
• Deep expertise across the AWS ecosystem, specifically:
• IoT: IoT Core, IoT Greengrass, IoT Analytics, device shadow, MQTT broker management
• Big Data: S3 (data lake), Glue, Athena, EMR, Kinesis (Data Streams / Firehose), Redshift, Lake Formation
• ML Pipelines: SageMaker (Training, Processing, Endpoints), Step Functions, Feature Store, Model Registry
• Compute & Orchestration: Lambda, ECS/EKS
• Data & Messaging: MSK (Kafka), SQS, SNS, DynamoDB,
• Strong understanding of distributed systems — event-driven architectures, CQRS, eventual consistency, back-pressure, and fault tolerance
• Experience designing data lakes and data mesh architectures — partitioning strategies, cataloging, and cost-efficient storage tiers
• Strong API design skills — REST, GraphQL, gRPC, or async event contracts
• Excellent communication — ability to convey complex architectural decisions clearly to both technical and non-technical stakeholders
• Experience producing and maintaining architecture documentation (C4 model, data flow diagrams, sequence diagrams)

Nice to Have

• Familiarity with LLM/GenAI pipelines — RAG architectures, vector databases, prompt engineering, fine-tuning, and model serving (e.g., Bedrock, SageMaker )
• Experience with MLOps tooling — MLflow, Kubeflow, Weights & Biases, or similar
• Knowledge of edge-cloud hybrid architectures — processing split between edge devices (e.g., NVIDIA Jetson) and cloud
• Experience with real-time streaming architectures
• Understanding of security frameworks — Zero Trust, encryption at rest/in transit, IAM policies, VPC design, FIPS compliance
• Background in autonomous systems, robotics, or defense domains