Principal AI Engineer Solution Delivery (Full-Time) 12+ Years of Experience need

KPG99 INC; United States; On-site; USD 150,000 - 190,000; Full time

Job summary

KPG99 INC is seeking a senior AI leader to drive end-to-end AI/ML and GenAI initiatives. You will collaborate with data, technology, product, and business teams to scope opportunities, design solutions, and productionize agentic AI systems with robust safety and monitoring.

Strong communication and mentoring skills are essential for guiding a high-performing team. The role requires hands-on experience with LLMs, RAG architectures, and cloud platforms, as well as the ability to prototype with

Qualifications

• Extensive experience applying data science, ML, and AI to real-world business problems.
• Fluent with AI-assisted engineering tools like Codex and Claude Code, used daily as a force multiplier with critical review of output.
• Full-stack AI fluency across modeling to production, including rapid prototyping with Streamlit and React.
• Independently ship and operate a solution on the platform, not just build infrastructure.
• Hands-on experience with LLMs, RAG architectures, and prompt engineering, including graph-based retrieval.
• Experience with multi-agent and sub-agent orchestration frameworks for production agentic systems.
• Proficiency with cloud platforms (GCP Vertex AI, AWS SageMaker/Bedrock) and Databricks.
• Strong stakeholder engagement and communication for aligning business and tech teams.
• Mentoring technical talent and leading AI initiatives end-to-end.
• Experience in regulated industries is a plus.
• Lead end-to-end AI initiatives from feasibility to production monitoring.

Responsibilities

• Lead AI/ML and GenAI initiatives end-to-end with cross-functional teams.
• Own end-to-end delivery for AI solutions on the enterprise AI platform, including CI/CD and IaC integration.
• Design and implement agentic AI systems with multi-agent orchestration and safety guardrails.
• Develop production-grade RAG and retrieval systems with advanced retrieval patterns.
• Set technical standards for AI development and evaluate new capabilities.

Skills

Data science

ML

AI

AI-assisted engineering tools

Codex

Claude Code

LLMs

RAG architectures

prompt engineering

Multi-agent orchestration

Agentic AI systems

Cloud AI platforms

GCP Vertex AI

AWS SageMaker

Bedrock

Databricks

Streamlit

React

Tools

LangGraph

Google ADK

CI/CD tools

IaC templates

Vertex AI tools

SageMaker

Job description

• Extensive experience applying data science, ML, and AI to real-world business problems, with demonstrated growth in scope, complexity, and influence.
• Fluent with AI-assisted engineering tools, such as Codex, Claude Code, used daily as a force multiplier, with critical review of generated output. Experience building engineering agents that accelerate delivery itself, such as testing, code review, or deployment-validation agents, not just using AI tools to write code.
• Full-stack AI fluency: demonstrated ability to work across the spectrum from statistical modeling and ML to LLM application development and agentic system design, including rapid prototyping (e.g., Streamlit, React) and owning solutions through to production.
• independently ship and operate a solution on the platform, not to build or own platform infrastructure.
• Deep hands-on experience with LLMs, RAG architectures, and prompt engineering, including graph-based retrieval (GraphRAG, knowledge graphs) alongside traditional vector-based RAG. Hands-on experience with multi-agent and sub-agent orchestration frameworks (e.g., LangGraph, Google ADK, or equivalent) and MCP or equivalent tool-integration protocols, for production agentic systems.
• Experience developing and evaluating AI systems rigorously: automated evaluation pipelines, red‑testing, hallucination detection, safety testing, and performance monitoring.
• Proficiency with cloud AI platforms, particularly GCP Vertex AI for agentic development, with working knowledge of AWS services (SageMaker, Bedrock) and modern data platforms (Databricks). Comfort consuming cloud-native platform services (existing IaC templates, CI/CD pipelines, the AI gateway) to deploy and operate solutions.
• Strong stakeholder engagement and communication skills: ability to scope initiatives, present to senior leaders, manage expectations, and drive alignment across business and technology partners.
• Track record of mentoring and elevating technical talent.
• Experience in life insurance, financial services, or other regulated industries is a plus.
• Lead AI/ML and GenAI initiatives end-to‑end, in partnership with data, technology, product, and business teams.
• Scope and shape initiatives from opportunity identification and feasibility assessment through solution design, delivery, and stakeholder alignment.
• Own end‑to‑end delivery for AI solutions, from build through evaluation, deployment, and production monitoring, shipping and operating them on the enterprise AI platform using its existing CI/CD, IaC templates, and gateway.
• Design and implement Agentic AI systems, including multi‑agent orchestration, tool use, memory architectures, human‑in‑the‑loop checkpoints, and safety guardrails. Build multi‑agent solutions with sub‑agents that plan, implement, validate, deploy, and log.
• Design and deploy production‑grade RAG and retrieval systems, including hybrid search, reranking, evaluation, and advanced retrieval patterns (agentic RAG, graph‑enhanced retrieval) as appropriate.
• Lead technical decisions across the AI stack: model selection, orchestration frameworks and tool‑integration protocols (e.g., LangGraph, MCP, or direct API integration), cloud AI platforms (GCP, AWS), and data platform (Databricks) integration.
• Rapidly prototype AI-powered applications and interfaces to validate ideas, test usability, and accelerate adoption, using modern frameworks (Streamlit, React, or similar).
• Build with AI‑assisted engineering tools daily (such as Codex, Claude Code) as a force multiplier, with critical review of all generated output. Build engineering agents that accelerate the team's own delivery, for example agents that assist with testing, code review, or deployment validation, not just use AI tools to write code.
• Define and evolve technical standards for AI development, including evaluation frameworks, testing practices, observability, and responsible AI principles. Evaluate and integrate emerging AI capabilities (new foundation models, agent frameworks, AI‑assisted development tools).
• Provide technical leadership and mentorship to data scientists and AI engineers, fostering a culture of rapid experimentation, rigorous evaluation, and continuous learning.
• This role involves limited travel (<10%) to events and vendor meetings