Responsibilities
- Deliver production agentic AI systems and AWS modernization engagements on committed dates across three pillars: Data Platform Modernization, Application & Database Modernization, and AI Agents for Apps.
- Build and productionize AI agents, orchestration, retrieval pipelines, evaluation harnesses, observability running against real customer data, not demo data.
- Convert existing products into agents: expose product functionality as callable tools for agent-to-agent composition, or replace form-and-click UX with agent-native, intent-driven interfaces.
- Convert existing Business processes into agents: expose process functionality as callable tools for agent-to-agent composition, or replace form-and-click UX with agent-native, intent-driven interfaces.
- Embed directly with customer engineering teams. Run architecture sessions, defend design decisions, and align stakeholders from VP Engineering to CTO.
- Make agent decisions traceable and defensible, validation runs in parallel with live workloads, and outputs hold up to internal audit and regulators (HIPAA, PCI-DSS, FSI-grade governance where the vertical demands it).
- Feed field experience back into the platform and practice: your deployment patterns, integration playbooks, and edge cases shape how we deliver.
Requirements
- Excellent communication skills (English) — verbal and written. Non-negotiable.
- You have shipped production agentic AI systems on AWS. Not POCs, not notebooks — systems running in production for real users.
- Deep understanding of agentic architecture — you can design an agent system from first principles and explain why each component exists.
- Strong AWS production experience: Amazon Bedrock and AWS AI services, plus core platform services (Lambda, API Gateway, DynamoDB, RDS/Aurora, Glue, EMR, Redshift, Kinesis, or similar depending on specialization).
- Solid software engineering fundamentals Python, TypeScript, CI/CD, infrastructure-as-code, testing-driven development discipline.
Nice to Have
- Experience with data or application modernization (database migration, legacy refactoring, data platform builds) is a strong plus, since agents run against these workloads.
- US English verbal and written fluency
- Delivery experience in one or more of our verticals: Financial Services, Healthcare & Life Sciences, Internet & Software, Manufacturing, or Telco/Media/Entertainment/Gaming/Sports.
- Model tuning and fine-tuning: systematic prompt engineering and optimization; parameter-efficient fine-tuning (LoRA/QLoRA or similar); instruction tuning; working knowledge of RLHF/DPO; sound judgment on when to fine-tune vs. prompt vs. RAG; and evaluation of tuned models against baselines. Fine-tuning experience on Amazon Bedrock or SageMaker is a plus.
- Experience with compliance-sensitive AI systems (HIPAA, PCI-DSS, SOC 2, data residency).
- Knowledge graph, code-analysis (AST), or CDC/streaming experience (Debezium, Kafka/MSK).
- Solid software engineering fundamentals — Java, C++, Go Lang, .Net, Rust
- Prior customer-facing consulting or forward-deployed experience.
- AWS certifications (Solutions Architect Professional, Machine Learning Specialty, or Data Analytics).
Additional Information
- English language requirements
- Customer-facing consulting experience preferred
- AWS certifications preferred
- US English fluency preferred
- Delivery experience in specific verticals preferred
