Forward Deployed Engineer - Agentic

Senior Forward Deployed Engineer (AI/ML)About the Job About Aligned Automation At Aligned Automation, we live by our "Better Together" philosophy to build a better world. As a strategic service provider to Fortune 500 companies, we help digitize enterprise operations and drive impactful business strategies. Our purpose goes beyond projects—we strive to deliver meaningful, sustainable change that shapes a more optimistic and equitable future. Our culture is deeply rooted in our 4Cs—Care, Courage, Curiosity, and Collaboration—ensuring that each employee is empowered to grow, innovate, and thrive in an inclusive workplace.Experience: 8–15 Years Forward Deployed Engineer – Agentic Transformation & AI-Native Portfolio Management Job Description: Senior Forward Deployed Engineer (AI/ML)About the Role We're seeking a Senior Forward Deployed Engineer who has evolved from traditional ML engineering into the modern AI stack, bringing a consulting mindset to customer-facing delivery. You'll embed with clients to design, build, and ship production AI systems—translating ambiguous business problems into deployed solutions.What You'll Do Embed directly with client teams to scope, prototype, and deploy AI-powered applications end-to-end Architect solutions using modern LLM tooling (agentic frameworks, RAG pipelines, orchestration layers) while applying rigorous ML fundamentals where they still matter Translate business requirements into technical roadmaps, then personally build the systems that deliver them Own the full lifecycle: discovery, POC, production hardening, evaluation, and handoff Serve as the technical bridge between client stakeholders and internal product/engineering teams Mentor client and pod engineers on AI-native development practicesWhat We're Looking For Core background 8+ years hands-on engineering, with demonstrated transition from classical ML (feature engineering, model training, MLOps) to the modern generative AI stack Prior consulting or client-facing delivery experience, comfortable with ambiguity, shifting scope, and stakeholder management Strong software engineering fundamentals (production Python, APIs, cloud deployment) Traditional ML foundation Experience building and deploying supervised/unsupervised models, feature pipelines, and evaluation frameworks Understanding of when classical approaches outperform LLMs (and the judgment to choose correctly) Modern AI stack Hands-on experience with LLM application development: prompt engineering, RAG, agentic workflows, tool use, and function calling Familiarity with orchestration frameworks (LangChain, LlamaIndex, or equivalent), vector stores, and evaluation/observability tooling Experience shipping LLM systems to production, including latency, cost, and reliability tradeoffs Consulting DNA Excellent written and verbal communication; can present to both engineers and executives Self-directed, able to lead engagements with minimal oversight Bias toward shipping working software over polished slidesWhat Success Looks Like Within 6 months, you've independently led at least two client engagements from discovery to production deployment, established repeatable delivery patterns, and become a trusted technical advisor to client leadership.