Generative AI Forward Deployed Engineer

Job description

Mactores is the agent-native AWS modernization firm. Most modernization work doesn't ship, it stalls in pilots, slips a year, or lands at three times the budget. We exist to ship it: production systems running, legacy retired, outcomes measured. Our delivery is built on Aedeon, the agent platform built by Mactores' founders' sister company, which absorbs the repetitive 60–70% of engagement work, discovery, dependency mapping, validation, test generation, that traditional consulting bills human hours against. Forward-deployed engineers own the rest: architecture, judgment, and cutover, on dates we commit to in the contract. Forward-deployed engineers (FDEs) are Mactores' services layer. You embed with the customer's team, own outcomes from discovery through the production cutover, and personally carry the delivery commitment. The agent platform we deploy absorbs 60–70% of engagement work, discovery, assessment, design, and testing. You absorb the judgment: target architecture, refactoring trade-offs, model selection, cutover strategy, and the decisions an agent platform cannot make. The agent absorbs scale. You absorb judgment. This is not a staff-augmentation seat and not an advisory role. You ship. ### What you will do? - 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. ### What are we looking for? - **Excellent communication skills (English) — verbal and written. Non-negotiable.** You will present architecture to customer CTOs, write documents that hold up in audit, and defend judgment calls in the room. **If you can build but not explain, this role is not a fit.** - **You have shipped production agentic AI systems on AWS.** Not POCs, not notebooks — systems running in production for real users. This is the primary qualification. Be prepared to walk through what you shipped, the decisions you made, and what broke. - **Deep understanding of agentic architecture** — you can design an agent system from first principles and explain why each component exists: - - Agent design patterns: single-agent vs. multi-agent systems, supervisor/orchestrator patterns, hierarchical agent topologies, planner–executor separation, and when each applies. - Orchestration: building and operating orchestrator agents that decompose tasks, route work to specialist agents or tools, and manage state across multi-step workflows (LangGraph, Strands Agents, CrewAI, or equivalent). - Memory: short-term/working memory (context management, conversation state) and long-term memory (episodic and semantic stores, vector- and graph-backed retrieval), and the production trade-offs of each. - Reflection and self-correction: critique loops, self-evaluation, retry-with-feedback patterns, and evaluation harnesses that catch agent failures before customers do. - Tool use and function calling: schema design, tool-selection reliability, error handling, and agent-to-agent composition. - RAG and retrieval pipelines: chunking, embedding, hybrid retrieval, reranking, and grounding agent decisions in customer data. - 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. - Experience with data or application modernization (database migration, legacy refactoring, data platform builds) is a strong plus, since agents run against these workloads. - Indicative experience: roughly 3–10 years in engineering roles, with agentic AI / GenAI as your current day job. We have demonstrated agent-native expertise over tenure — an engineer with 3–4 years of hands-on agentic AI work typically outperforms a 12-year generalist on this work. ### You'll be preferred if you've: - 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). ### Why This Role? - **You own outcomes, not tickets.** FDEs carry the delivery commitment personally — architecture, judgment, and cutover are yours. - **You work agent-native from day one.** Our delivery model would not function without agents. You build with the platform, not around it. - **You ship.** Engagements measured in weeks to production, legacy retired, outcomes named. No archived pilots. - **You compound.** Field delivery informs the Aedeon platform roadmap; the platform's growth expands what you can deliver. Few engineering roles sit in that loop.