Department: Engineering / Data & AI
Location: Visakhapatnam, AP
Type: Full-time
About Aerosimple
Aerosimple helps airports streamline airfield inspections, safety/compliance, and operations through configurable workflows, mobile-first tools, and actionable reporting. Our platform supports airport teams in standardizing inspections, improving response times, and maintaining regulatory readiness through better digital processes.
We're building intelligent features that reduce manual effort, surface operational insights, and improve decision-making for airport teams.
Role Summary
We are looking for a Backend AI Programmer with strong Python experience to design, build, and deploy AI-powered services that enhance the Aerosimple platform. This role will focus on building scalable backend systems for AI/ML workflowssuch as intelligent issue categorization, anomaly detection, summarization, search, recommendations, and automationwhile collaborating closely with product and engineering teams.
You will work with real-world airport operational data and build services that are secure, reliable, and production-ready.
Key Responsibilities AI/ML Backend Development
- Build and maintain backend services that power AI/ML features within Aerosimple.
- Develop pipelines for data preprocessing, feature extraction, model training, evaluation, and inference.
- Deploy models into production using scalable APIs and background processing workflows.
- Implement monitoring and observability for AI services (latency, cost, drift, accuracy, failure modes).
- Work on use cases such as:
- Smart inspection insights (trends, recurring issues, risk hot spots)
- Automated issue tagging / classification / prioritization
- Summaries of inspection reports and corrective actions
- Predictive or proactive alerts based on inspection/operations patterns
- Natural language search over inspection findings and operational logs
Backend Engineering & Platform Integration
- Design clean APIs and integrate AI services into existing web/mobile workflows.
- Work with event-driven architectures and async processing (queues, workers, scheduling).
- Design data models and storage strategies for AI/ML outputs (predictions, embeddings, explanations, audit logs).
- Ensure AI features are secure and comply with customer requirements (data access controls, audit trails).
- Collaborate with backend engineering to ensure consistency in performance, reliability, and code quality.
Data & Quality
- Partner with product and implementation teams to understand airport workflows and identify high-value AI opportunities.
- Improve data quality through validation, normalization, and automated checks.
- Build tools and internal utilities for labeling, model evaluation, and experimentation.
Collaboration & Documentation
- Work closely with Product, Customer Success, and Engineering stakeholders to define requirements and iterate quickly.
- Document system design, model behavior, limitations, and operational runbooks.
- Participate in code reviews and help raise engineering standards across AI/backend work.
What Success Looks Like (Outcomes)
- AI services ship reliably in production and improve customer workflows measurably.
- Models are deployable, observable, and maintainable (not research-only).
- Inference costs and latency are controlled and optimized.
- AI features are explainable and auditable enough to work in compliance-heavy airport environments.
- AI capabilities integrate seamlessly into the Aerosimple product experience.
Required Qualifications
- 3+ years of professional experience in backend development with Python.
- Strong proficiency in building APIs and services (e.g., FastAPI, Flask, Django REST).
- Experience working with data pipelines and ML workflows using Python (e.g., Pandas, NumPy, scikit-learn, PyTorch/TensorFlow a plus).
- Experience deploying ML/AI systems in production (model serving, inference endpoints, CI/CD, containerization).
- Solid understanding of databases and data modeling (PostgreSQL, MySQL, etc.).
- Experience with cloud infrastructure (AWS/GCP/Azure) and Docker-based deployment.
- Strong engineering fundamentals: testing, code reviews, debugging, scalability, and performance.
Preferred Qualifications
- Experience with LLM-driven systems (prompting, RAG, embeddings, vector DBs, evaluation).
- Experience with background workers and messaging queues (Celery, Redis, RabbitMQ, Kafka, SQS).
- Familiarity with observability tooling (OpenTelemetry, Prometheus, Grafana, Datadog).
- Experience building multi-tenant SaaS systems with strong role-based access control (RBAC).
- Domain experience in inspections, compliance, asset management, facilities, logistics, or aviation operations.
- Exposure to privacy/security practices for enterprise customers (SOC2-style controls).
Tech Stack (example adapt to Aerosimple's stack)
- Python, FastAPI
- PostgreSQL, Redis
- Docker, CI/CD
- Cloud: AWS/GCP
- ML stack: scikit-learn / PyTorch / Hugging Face (as applicable)
- Vector DB / embeddings (as applicable)
Why Join Aerosimple
- Build AI features that solve real operational problems in airports and aviation safety.
- Opportunity to work end-to-end: from model development to production deployment.
- Product-focused team where engineering decisions directly impact customer outcomes.
- High ownership and the chance to shape Aerosimple's AI direction.
