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.