Who We Are:

For enterprises struggling to secure cloud workloads, Aviatrix® offers a single solution for pervasive cloud security. Where current cybersecurity approaches focus on securing entry points to a trusted space, Aviatrix Cloud Native Security Fabric (CNSF) delivers runtime security and enforcement within the cloud application infrastructure itself – closing gaps between existing solutions and helping organizations regain visibility and control. Aviatrix ensures security, cloud, and networking teams are empowering developer velocity, AI, serverless, and what's next. For more information, visit www.aviatrix.com.

About the Role – Senior Member of Technical Staff – Software Development

Our SaaS platform serves enterprise customers with a reliable, secure, and scalable product built on modern microservices, event-driven data pipelines, and AI-powered capabilities.

We are seeking a Senior Member of Technical Staff (Sr MTS) – Software Development to join our SaaS Platform team. This role is ideal for a backend engineer who has moved beyond writing individual features and is ready to take meaningful ownership of platform components, contribute to technical design, and start influencing the direction of the team. You will work alongside Staff and Principal Engineers on building microservices, data pipelines, and AI-integrated backend systems on AWS — developing both your technical depth and your ability to lead within a team.

Responsibilities

Feature Development & Ownership

Take end-to-end ownership of backend features and services — from requirements and design through implementation, testing, deployment, and monitoring.
Design and implement production-grade microservices in Golang, following team standards for gRPC and RESTful API patterns.
Contribute meaningfully to technical design discussions; author design documents for features and components you own, incorporating feedback from senior engineers.
Write well-tested, maintainable code; champion testing practices including unit, integration, and end-to-end tests within your feature area.
Participate in on-call rotations; debug and resolve issues in distributed backend services and contribute to post-incident reviews.

Data Pipelines & AI Integration

Build and extend data pipeline components that process, enrich, and route platform telemetry and events using AWS streaming and batch services (Kinesis, MSK, Glue, Step Functions).
Integrate AI and LLM capabilities into backend services and pipeline stages — including model inference calls, output parsing, and result handling — under the guidance of Staff Engineers.
Implement agentic AI workflow components such as tool-use handlers, retrieval-augmented generation (RAG) steps, and prompt orchestration layers as part of broader pipeline designs.
Contribute to the reliability and correctness of AI pipeline components through thorough testing, output validation, and adherence to team guardrail standards.

Platform Reliability & Operations

Ensure backend services you build are observable from day one: structured logging, metrics, distributed tracing, and alerting are treated as part of the definition of done.
Apply AWS networking and security best practices in the services you build: secure IAM roles, VPC-aware service communication, and encryption in transit and at rest.
Proactively identify and address reliability gaps, performance bottlenecks, and technical debt in platform components you own.
Support deployment of services on AWS using team CI/CD pipelines and infrastructure-as-code tooling.

Collaboration & Team Contribution

Collaborate effectively with Staff and Principal Engineers, product managers, and peers across a geographically distributed team.
Conduct thorough code reviews and provide constructive, actionable feedback; help MTS engineers grow through mentorship on your area of expertise.
Document APIs, service behavior, data flows, and runbooks clearly so teammates can understand, operate, and evolve your work.
Participate actively in planning, estimation, and sprint ceremonies; raise technical risks and dependencies early.

Requirements

Experience & Education

3+ years of professional software engineering experience in backend development.
Bachelor's degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience).
Solid foundation in computer science fundamentals: data structures, algorithms, and operating systems.
Demonstrated experience owning and shipping backend features or services in a production cloud environment.
Experience working in collaborative, agile engineering teams; comfortable operating in fast-paced, high-growth environments.

Backend & Microservices

Proficiency in Golang or a comparable strongly typed backend language; willingness to work primarily in Golang.
Working understanding of microservices architecture: service boundaries, inter-service communication via REST and gRPC, and the trade-offs of distributed systems.
Experience designing and implementing APIs with attention to correctness, reliability, and ease of use by other services.
Familiarity with event-driven patterns and messaging systems such as Kafka, Kinesis, or SQS/SNS.
Strong testing practices: unit, integration, and contract testing for backend services.

Agentic AI & LLM Integration

Working knowledge of large language models (LLMs) and how they are integrated into backend systems via API calls (e.g., Amazon Bedrock, OpenAI, or Anthropic APIs).
Familiarity with agentic AI concepts: tool use, multi-step reasoning, and agent orchestration frameworks such as LangChain, LlamaIndex, or Amazon Bedrock Agents.
Ability to implement prompt templates, manage context windows, and handle model outputs reliably within backend service logic.
Understanding of retrieval-augmented generation (RAG) patterns: embedding generation, vector store queries, and context injection into prompts.
Awareness of production considerations for AI components: output validation, error handling, latency management, and cost awareness.

Data Pipelines

Practical experience building or contributing to data pipelines for real-time or batch processing workloads.
Familiarity with AWS data pipeline services: Amazon Kinesis, AWS Glue, Amazon MSK (Kafka), AWS Step Functions, or Amazon EMR.
Understanding of how to embed AI/ML model inference calls as processing steps within a data pipeline.
Ability to add pipeline observability: tracking throughput, latency, error rates, and data quality metrics.

AWS & Cloud Infrastructure

Hands-on experience deploying and operating backend services on AWS; familiarity with core services including EC2, ECS/EKS, S3, RDS, and SQS/SNS.
Basic understanding of AWS networking and security: VPC, security groups, IAM roles and policies, and encrypted communications.
Experience with containerization (Docker) and deploying services in container-based environments.
Comfort working with CI/CD pipelines, Git-based workflows, and infrastructure-as-code tooling (Terraform or AWS CDK is a plus).

Nice to Have

Experience with Kubernetes (EKS) for container orchestration.
Hands-on use of Amazon Bedrock or equivalent managed AI platforms for LLM inference.
Experience with vector databases or embedding stores (OpenSearch, pgvector, Pinecone).
Exposure to AWS managed data services such as Amazon Redshift, DynamoDB, or Aurora.
Background in network security, cloud networking, or multi-cloud environments.
Interest in or exposure to ML model lifecycle management and MLOps practices.

Interpersonal & Communication

Clear written and verbal communication; able to write design documents, articulate technical decisions, and give constructive code review feedback.
Collaborative team player who works well in cross-functional, geographically distributed teams.
A growth mindset — eager to deepen expertise, take on new challenges, and mentor peers as you develop.