About the Role

As an AWS Forward Deployment Engineer (FDE), you will sit at the intersection of applied AI engineering and hands-on customer partnership. You will embed directly with our most strategic enterprise customers to design, prototype, and deliver production-grade AI solutions on AWS — building agentic systems with Amazon Bedrock, generative AI frameworks, and advanced orchestration tooling, writing expert-level Python, and moving fast enough to unblock customers in hours, not weeks. This is not a pre-sales or support role: you are an L3-caliber software engineer and AI practitioner who works at the frontier of what AWS makes possible, turning customer problems into intelligent, scalable solutions.

Core Competencies

Every AWS FDE is expected to demonstrate mastery across four foundational competencies:

AI Engineering

Deep expertise in AI and machine learning systems

Full-Stack Delivery

End-to-end solutions from front-end through back-end

Rapid Prototyping

Build and iterate on proofs of concept with speed

Customer-Centric

Translate customer needs into precise technical solutions

What You’ll Do

• Embed with enterprise customers to scope, architect, and deliver end-to-end AI solutions on AWS — from data layer through front-end interface

• Design and build agentic AI systems using Amazon Bedrock, LangChain, CrewAI, and orchestration frameworks — including multi-agent workflows, tool use, memory, grounding, and RAG architectures

• Write expert-level Python to develop, train, evaluate, and deploy ML models and AI pipelines using Amazon SageMaker

• Prototype rapidly — solve customer-blocking technical scenarios (e.g., custom audio connectors, API integrations, event-driven workflows, or data ingestion pipelines) within hours when required

• Build full-stack cloud-native solutions spanning Amazon S3, Lambda, API Gateway, EventBridge, ECS/EKS, Step Functions, DynamoDB, OpenSearch, Redshift, and SageMaker to deliver integrated, production-ready systems

• Lead technical discovery sessions to identify high-value AI use cases, assess data readiness, and define measurable success criteria with customers

• Implement MLOps best practices using SageMaker Pipelines, model monitoring, feature stores, automated CI/CD workflows, and infrastructure-as-code

• Leverage AWS AI services — Amazon Textract, Transcribe, Rekognition, Comprehend, Polly, Translate, and Bedrock foundation models — for rapid capability delivery where custom models are not required

• Advise customers on responsible AI, model explainability, security, governance, and compliance using AWS-native tooling and best practices

• Feed customer insights back to Fusion’s engineering and product teams to shape our AWS AI practice

• Produce clear architecture diagrams, technical runbooks, deployment guides, and handoff documentation for every engagement

• Travel to customer sites as needed (typically up to 30%)

What We’re Looking For

Required Qualifications

• L3 SWE Proficiency: Expert-level Python coding ability, including software design patterns, performance optimization, testing, debugging, and production-grade code quality

• Agentic Fluency: Hands-on experience building AI agents using Amazon Bedrock, LangChain, CrewAI, AutoGen, or equivalent frameworks — including tool calling, agent memory, orchestration, and RAG pipelines

• Rapid Prototyping: Demonstrated ability to solve ambiguous, blocking customer scenarios quickly by building working prototypes and integrations in hours

• Full-Stack Delivery: Experience delivering end-to-end solutions across the stack, from data ingestion and model inference through APIs and user-facing interfaces

• 5+ years of professional software engineering experience, with at least 1 year deploying AI/ML solutions on AWS or equivalent cloud platform

• Hands-on experience with Amazon SageMaker: training jobs, endpoints, pipelines, model deployment, feature store, and model monitoring

• Strong understanding of AWS data services: S3, Glue, Redshift, Athena, Kinesis, DynamoDB, and OpenSearch

• Familiarity with AWS infrastructure fundamentals: IAM, VPC, ECS/EKS, Lambda, CloudWatch, CloudFormation/Terraform, and networking/security concepts

• Experience building scalable APIs and microservices using REST, GraphQL, or event-driven architectures

• Excellent communication skills — able to present complex AI system behavior and trade-offs to both technical teams and executive stakeholders

Nice to Have

• AWS Certified Machine Learning – Specialty, AWS Solutions Architect Professional, or AWS DevOps Engineer certification

• Experience with multimodal AI capabilities — vision, audio, video, and document understanding

• Background in NLP, computer vision, conversational AI, recommendation systems, or time-series forecasting in production environments

• Familiarity with vector databases and RAG tooling such as Pinecone, Weaviate, FAISS, OpenSearch Vector Engine, or ChromaDB

• Experience with Bedrock Agents, Knowledge Bases for Bedrock, and Amazon Q

• Experience with observability and monitoring tools such as Prometheus, Grafana, Datadog, or CloudWatch

• Experience with data visualization platforms such as QuickSight, Tableau, or Power BI for AI-driven analytics products

• Prior customer-facing experience in applied AI/ML consulting, cloud engineering, or professional services environments

• Knowledge of responsible AI practices including fairness metrics, explainability frameworks (SHAP/LIME), governance, and model risk management

What Success Looks Like

In your first 30 days, you’ll shadow existing AWS customer engagements, get hands-on with our AI delivery methodology, and build your first Bedrock-powered agent or SageMaker pipeline. Within 90 days, you’ll be leading your own engagements end-to-end — from use case discovery through full-stack deployment. Within 6 months, you’ll be the go-to technical authority on agentic AI delivery at Fusion, influencing how we build our AWS practice and setting the bar for rapid, high-quality AI deployment for enterprise customers.

Why This Role

• Frontier work — you’ll build with Amazon Bedrock, SageMaker, and the latest AWS AI capabilities as they emerge

• Speed — unlike traditional consulting, you ship working AI solutions in days, not quarters

• Full-stack impact — you own the entire solution, from model to interface, with direct customer visibility

• AWS partnership — close collaboration with AWS solution architects and cloud engineering teams, including exposure to early-access AI capabilities

• Ownership — you run engagements end-to-end with high autonomy and direct relationships with customer technical leadership

• Career growth — a natural path toward AI Architect, AWS Practice Lead, Principal AI Engineer, or Head of AI Engineering roles