Job Title: Cloud Solution Architect with Strong AI/ML exp

Location: Remote

Experience: 12+ Years

Duration: Long Term

About the Role:

As an AWS Forward Deployment Engineer (FDE) at Fusion Global Solutions, 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/ML solutions on AWS—building agentic systems with Amazon Bedrock, training and deploying models on SageMaker, and writing expert-level Python to move 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 complex customer problems into intelligent, scalable cloud solutions.

Core Competencies:

• AI Engineering: Deep expertise in AI and machine learning on AWS
• Full-Stack Delivery: End-to-end solutions from the data layer to the user interface
• Rapid Prototyping: Build and iterate on proofs of concept quickly
• Customer-Centric: Translate customer needs into precise technical solutions

Responsibilities:

• Embed with enterprise customers to scope, architect, and deliver end-to-end AI/ML solutions on AWS—from data layer through front-end interface
• Design and build agentic AI systems using Amazon Bedrock—including multi-agent orchestration, RAG pipelines, knowledge bases, guardrails, and fine-tuned foundation models
• Write expert-level Python to develop, train, evaluate, and deploy ML models using Amazon SageMaker, including Pipelines, Feature Store, Model Registry, and Model Monitor
• Prototype rapidly—solve customer-blocking technical scenarios within hours when required
• Lead technical discovery sessions to identify high-value AI/ML use cases, assess data readiness, and define measurable success criteria with customers
• Implement MLOps best practices on SageMaker: automated retraining pipelines, A/B testing, drift detection, model versioning, and CI/CD for ML workflows
• Leverage AWS AI services to rapidly deliver capabilities when custom models are not required
• Advise customers on responsible AI practices including bias detection, model explainability, and governance using AWS built-in tooling
• Produce clear architecture diagrams, technical runbooks, and handoff documentation for every engagement

Requirements:

Required Qualifications (L3 SWE Proficiency):

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

Agentic Fluency:

• Hands-on experience building agentic AI systems using Amazon Bedrock, LangChain, and/or LlamaIndex

Rapid Prototyping:

• Demonstrated ability to solve ambiguous, blocking customer scenarios quickly by building working prototypes in hours

Full-Stack Delivery:

• Experience delivering end-to-end solutions across the stack
• 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, real-time endpoints, Pipelines, Feature Store, and/or Model Monitoring
• Strong understanding of AWS data services: S3, Glue, Athena, Redshift, Kinesis, and Lake Formation

Solid grasp of AWS infrastructure fundamentals:

• IAM, VPC, Lambda, CloudWatch, and API Gateway
• 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 or AWS Certified AI Practitioner certification
• Experience with Amazon Bedrock multimodal capabilities
• Background in NLP, computer vision, conversational AI, or time-series forecasting
• Familiarity with additional agentic frameworks
• Prior customer-facing experience in an applied AI or ML engineering role
• Knowledge of responsible AI practices

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 standard for rapid, high-quality AI deployment for our enterprise customers.

Why This Role:

• Frontier work
• Speed
• Full-stack impact
• AWS partnership
• Ownership
• Career growth