Forward Deployed Engineer – Agentic

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 practices

What 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 slides

What 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.