Solutions Architect
Indexed description
Key job responsibilities
- End-to-End Agentic AI Deployment
- Lead discovery workshops with customer CTO / engineering teams to map high-value AI use cases across multiple industry verticals.
- Own technical scoping, solution architecture for agentic AI workloads on Amazon Bedrock and AgentCore.
- Build PoCs leveraging Amazon Bedrock, Strands Agents SDK, Amazon AgentCore
- Design and implement multi-agent orchestrations, MCP tool servers, RAG pipelines (Knowledge Bases for Bedrock, OpenSearch Serverless, Aurora pgvector), and LLM-as-judge evaluation frameworks.
- Deliver IaC (AWS CDK / CloudFormation / Terraform) for repeatable, production-grade deployment patterns that the customer can own post-engagement.
- Regulated Industry Delivery
- Navigate India's regulatory and translate requirements into Bedrock Guardrails, Automated Reasoning Checks, and data residency configurations.
- Architect AI systems for auditability: model version pinning, prompt logging, PII redaction, inference geography (ap-south-1), and compliance artifact generation.
- Deliver eval-driven acceptance criteria: define benchmark suites, run eval loops against domain-specific test sets, and produce launch-evidence packages that satisfy customer risk / model governance teams.
- Customer Engineering Partnership
- Run structured discovery (jobs-to-be-done, process mining, failure mode analysis) to identify AI leverage points and sequence delivery for maximum early impact.
- Build long-term technical relationships with customer engineering leadership and proactively surface new AI deployment opportunities across the account lifecycle.
- AWS Field Signal & Product Influence
- Codify repeatable deployment patterns into AWS-wide playbooks, reference architectures, and GitHub samples that scale insights across hundreds of customers.
- Feed structured field signal (model gaps, tooling friction, feature requests, eval results) to AWS Engineering teams
- Collaborate with AWS SA leadership, Specialists, Partner SA, and ISV teams to deliver joint engagements and avoid duplicating effort.
- Represent AWS AI at customer EBCs, industry conferences, and CXO briefings — you are the technical face of AWS's GenAI capability in the field.
- 3+ years of working with or evaluating AI systems experience
- Experience working directly in customer implementations
- Experience in a technical position
- Production experience with LLMs: advanced prompt engineering, multi-agent architectures, RAG pipeline design, evaluation frameworks, and inference optimization.
- Strong programming skills in Python; proficiency in at least one of TypeScript/Java/Golang. Comfortable writing production-grade code, not just notebooks.
- Hands-on experience with AWS AI/ML services: Amazon Bedrock (Knowledge Bases, Guardrails, Model Customization), SageMaker, and the broader AWS stack (Lambda, ECS/EKS, RDS/Aurora, OpenSearch, Step Functions).
- Experience designing distributed systems at enterprise scale: API design, async patterns, event-driven architectures, security (IAM, VPC, KMS), and observability (CloudWatch, X-Ray, OTEL).
- Demonstrated ability to embed with enterprise customer teams and deliver working prototypes under ambiguity, time pressure, and evolving requirements.
- Strong communication skills: able to run technical discovery with engineering teams and translate tradeoffs into decisions for CTO/CIO audiences.
- You identify the right next action without waiting for direction, and you drive clarity rather than seeking it.
- Experience working within software development or Internet-related industries
- Experience migrating or transforming legacy customer solutions to the cloud
- Experience working with AWS technologies from a dev/ops perspective
Company - AWS India - Karnataka
Job ID: A10456171
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