Python/GenAI Solutions Architect
New
USFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 7+ years
- Required Skills
- AWSDockerPythonKubernetesFastAPICI/CDLLMGenerative AI
Requirements
- 7+ years of experience designing and delivering production-grade software systems and cloud-based applications.
- Strong expertise in Python development, including OOP principles, clean architecture, design patterns, and performance optimization.
- Hands-on experience building and deploying AI/ML solutions, including LLM integrations, RAG architectures, and agentic workflows.
- Proven experience with frameworks such as FastAPI, Django REST Framework, or Flask for building scalable APIs.
- Solid understanding of cloud platforms, especially AWS services such as Bedrock, SageMaker, Lambda, ECS, S3, SQS, and ECR.
- Experience with Docker, Kubernetes, CI/CD pipelines, testing frameworks, and modern Python tooling.
- Ability to lead technical discovery, manage architecture trade-offs, estimate delivery timelines, and optimize infrastructure costs.
- Excellent communication and stakeholder management skills with the ability to present technical concepts to both technical and non-technical audiences.
Responsibilities
- Design, develop, and optimize scalable Python-based backend systems, AI integrations, RESTful APIs, and cloud-native applications.
- Lead the implementation of production-ready RAG systems, LLM-powered solutions, and agentic AI workflows tailored to client business needs.
- Own the technical direction of client engagements from solution discovery and architecture design through deployment and delivery.
- Support presales activities including technical discovery calls, scoping exercises, architecture proposals, and client demonstrations.
- Produce technical documentation, architecture standards, and solution blueprints while leading architecture reviews and decision-making processes.
- Collaborate closely with engineering, product, and client stakeholders to ensure high-quality, scalable, and cost-effective solutions.
- Mentor developers through code reviews, technical coaching, and knowledge-sharing initiatives across the engineering organization.
- Build trusted advisor relationships with client stakeholders by providing strategic technical guidance and AI expertise.
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