| Location | ATL, GA |
| Job Type | Full-time, Employee |
| Salary | $0–$63 Per Hour |
| Headquarters | ATL, GA, US |
Forward Deployed Engineer (FDE) - AI / GenAI
Location: Atlanta GA(Hybrid)
Work Mode :
50% Hybrid Work From Office Schedule
The role follows a 50% hybrid work model. Employees are required to work from the office for 5 consecutive business days, from Thursday through the following Wednesday, on alternate weeks.
| Role Summary | |
| As a Forward Deployed Engineer you will embed directly with airline business and operations teams to identify high-value problems and rapidly build, deploy and scale AI-powered solutions on AWS. You sit at the intersection of engineering, product and the customer: you discover the problem onsite, prototype quickly with GenAI and agentic technologies (Amazon Bedrock, AgentCore, MCP), and own the path to a secure, production-grade solution that moves operational and customer metrics. | |
| Key Responsibilities | |
| Work onsite with stakeholders across flight operations, crew, TechOps/maintenance, airports, customer service and corporate functions to discover and prioritize AI use cases | |
| Translate ambiguous business problems into technical designs, working prototypes and production deployments, often within weeks | |
| Design and build GenAI and agentic solutions using Amazon Bedrock, AgentCore and frameworks such as LangGraph / LangChain / Strands Agents | |
| Build MCP-based integrations and APIs that connect AI agents to airline systems of record, operational data and enterprise tools | |
| Implement RAG pipelines over manuals, policies, maintenance records and operational data; tune retrieval quality | |
| Establish evaluation, guardrails and observability for LLM workloads; optimize latency, accuracy and cost | |
| Deliver using IaC (Terraform / CloudFormation / CDK), CI/CD and serverless / containerized patterns aligned with enterprise standards | |
| Partner with AI Architects and AI Engineers: feed field learnings back into reusable components, patterns and platform roadmap | |
| Present solutions, demos and outcomes to technical teams and business leadership; drive user adoption and iteration | |
| Ensure solutions meet security, privacy (PII) and regulatory requirements of the airline environment | |
| Required Qualifications | |
| 5+ years hands-on software engineering, delivering production applications on AWS Cloud | |
| Airline industry experience: delivered technology solutions in an airline environment (flight ops, crew scheduling, TechOps/MRO, airport ops, revenue management, customer service / loyalty) | |
| Forward-deployed / customer-facing engineering: embedded with business & operations teams onsite, turning ambiguous problems into shipped solutions | |
| Strong programming in Python plus one of Java, TypeScript or Node.js (back-end depth is MUST, full-stack ability expected) | |
| Built and shipped GenAI / LLM solutions to production (beyond POCs) with measurable business outcomes | |
| Hands-on with AWS AI services, specially Bedrock / AgentCore (personally built, not only evaluated) | |
| Agentic frameworks in production (LangChain, LangGraph, Strands Agents or similar) | |
| MCP-based integrations connecting AI agents with enterprise tools, APIs and data | |
| RAG pipelines: chunking, embeddings, vector stores (OpenSearch, pgvector or similar), retrieval tuning | |
| Prompt engineering and LLM evaluation (eval datasets, guardrails, hallucination & regression testing) | |
| Proficiency in AWS core services (S3, Lambda, API Gateway, RDS/DynamoDB, Step Functions, EventBridge) | |
| Serverless & microservices architecture; containerization (Docker, Kubernetes / ROSA) | |
| Integrating with legacy / enterprise systems of record (REST/SOAP APIs, messaging, batch & streaming data) | |
| Infrastructure as Code (Terraform, CloudFormation or CDK) and CI/CD pipelines & DevOps practices | |
| Experience using AI-assisted dev tools, preferably Kiro, for coding/testing/docs/debugging | |
| Rapid prototyping: demo-able MVP in days/weeks, then hardening it for production (scalability, reliability) | |
| Observability, latency and cost optimization of LLM workloads (CloudWatch, tracing, token/cost tracking) | |
| Application security, IAM and data privacy for AI solutions (PII handling, least privilege, responsible AI) | |
| Requirements discovery and communication: whiteboarding with users, presenting to technical and executive audiences | |
| Strong ownership and problem-solving in fast-paced, ambiguous environments | |
| Preferred Qualifications | |
| Bachelor's degree in CS, IS, Engineering or related field | |
| Familiarity with airline systems & standards (PSS such as Amadeus/Sabre, NDC, IATA messaging, ACARS) | |
| AWS certifications (Solutions Architect, Developer, Machine Learning / GenAI related) | |
| Multi-agent orchestration patterns, model fine-tuning or SageMaker experience | |
| Front-end skills (React or similar) for quick user-facing tools and demos | |
| Experience in regulated environments (FAA, DOT, TSA, PCI-DSS) | |