Agentic AI Developer- Lead

Job highlights Identified by Google from the original job post Qualifications • Public vs. private LLMs • 8+ years in software architecture, AI engineering, or technical strategy • Proven experience designing and deploying AI/ML systems in production • Deep understanding of: • LLM ecosystems (OpenAI, open-source models, fine-tuning strategies) • Agent frameworks and orchestration tools • Cloud-native architecture (AWS, Azure, or GCP) • Data pipelines and API-driven systems • Experience evaluating private vs. public model trade-offs • Strong executive communication skills • Demonstrated ability to move from whiteboard strategy to deployed solution • 8 more items(s) Responsibilities • In this role you will define and lead the companies AI transformation strategy setting the vision, identifying high-impact use cases, and translating opportunity into execution • This role sits at the intersection of strategy and delivery • Advise leadership on where and how AI should be adopted • Design the technical architecture that supports AI initiatives • Evaluate and recommend the right AI approaches (Agents, LLMs, private vs. public models, automation frameworks, etc.) • Lead pilots and help bring solutions into production • You are both the architect and the builder, equally comfortable shaping executive strategy and rolling up your sleeves to prototype, implement, and refine solutions • Define Strand’s AI roadmap aligned to business objectives • Identify high-value AI use cases across internal operations and client engagements • Assess build vs. buy decisions across AI tools and platforms • Establish governance frameworks for responsible AI adoption • Advise executives on risk, data privacy, compliance, and operating model design • Design scalable AI architectures across cloud and hybrid environments • Recommend appropriate model strategies: • Hosted vs. self-deployed models • Agent-based systems • Retrieval-Augmented Generation (RAG) frameworks • Define standards for data pipelines, API integrations, orchestration layers, and security • Lead technical evaluations of emerging AI tools and platforms • Make principled trade-offs between speed, cost, risk, and scalability • Lead pilot programs from hypothesis through production • Build and deploy AI workflows, agents, or automation systems • Partner with engineering, data, and product teams to operationalize AI • Establish measurable KPIs tied to ROI and business outcomes • Ensure architectural governance and technical documentation • Influence & Leadership • Act as a trusted advisor to executives and technical stakeholders • Translate complex AI concepts into clear business implications • Mentor engineers and consultants in AI architecture and implementation • Foster alignment between strategy, product, and engineering teams • Support go-to-market initiatives with thought leadership and client advisory • 28 more items(s) More job highlights Job description Our client, a leading engineering firm is seeking an AI Strategist / Architect.

In this role you will define and lead the companies AI transformation strategy setting the vision, identifying high-impact use cases, and translating opportunity into execution.

This role sits at the intersection of strategy and delivery. You will:

• Advise leadership on where and how AI should be adopted

• Design the... technical architecture that supports AI initiatives

• Evaluate and recommend the right AI approaches (Agents, LLMs, private vs. public models, automation frameworks, etc.)

• Lead pilots and help bring solutions into production

You are both the architect and the builder, equally comfortable shaping executive strategy and rolling up your sleeves to prototype, implement, and refine solutions.

Key Responsibilities

AI Transformation Strategy

• Define Strand’s AI roadmap aligned to business objectives

• Identify high-value AI use cases across internal operations and client engagements

• Assess build vs. buy decisions across AI tools and platforms

• Establish governance frameworks for responsible AI adoption

• Advise executives on risk, data privacy, compliance, and operating model design

Architecture & Technical Leadership

• Design scalable AI architectures across cloud and hybrid environments

• Recommend appropriate model strategies:

• Public vs. private LLMs

• Hosted vs. self-deployed models

• Agent-based systems

• Retrieval-Augmented Generation (RAG) frameworks

• Define standards for data pipelines, API integrations, orchestration layers, and security

• Lead technical evaluations of emerging AI tools and platforms

• Make principled trade-offs between speed, cost, risk, and scalability

Execution & Delivery

• Lead pilot programs from hypothesis through production

• Build and deploy AI workflows, agents, or automation systems

• Partner with engineering, data, and product teams to operationalize AI

• Establish measurable KPIs tied to ROI and business outcomes

• Ensure architectural governance and technical documentation

Influence & Leadership

• Act as a trusted advisor to executives and technical stakeholders

• Translate complex AI concepts into clear business implications

• Mentor engineers and consultants in AI architecture and implementation

• Foster alignment between strategy, product, and engineering teams

• Support go-to-market initiatives with thought leadership and client advisory

Qualifications

• 8+ years in software architecture, AI engineering, or technical strategy

• Proven experience designing and deploying AI/ML systems in production

• Deep understanding of:

• LLM ecosystems (OpenAI, open-source models, fine-tuning strategies)

• Agent frameworks and orchestration tools

• Cloud-native architecture (AWS, Azure, or GCP)

• Data pipelines and API-driven systems

• Experience evaluating private vs. public model trade-offs

• Strong executive communication skills

• Demonstrated ability to move from whiteboard strategy to deployed solution

Preferred

• Experience leading enterprise AI transformation initiatives

• Background in consulting or advisory environments

• Familiarity with governance, compliance, and AI risk management

• Exposure to multi-tenant SaaS or data-rich platforms

• Certifications in cloud architecture or AI engineering Show full description Report this listing Loading...