Forward Deployed Engineer – Agentic

Aligned Automation Services Pvt LtdAustin, TX

About The Position

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.

Requirements

  • 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)
  • 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)
  • 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
  • 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

Responsibilities

  • 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
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