AI Agent Platform Development Roles

Like all technological revolutions, work will change. According to this article, there are 16 new roles that have been created from AI: User Experience Roles AI Conversation Designer: Designs dialogue, tone, and flow of AI interfaces; blends UX writing, psychology, and prompt design. Knowledge Architect: Structures and maintains AI’s knowledge base; ensures accurate, context-aware responses. Interaction Designer: Builds models for human–AI interaction, focusing on trust and collaboration; skilled in conversational UX and explainability. AI Artist Engineer: Uses AI to create strategic, visually aligned creative content for brands. Prompt Engineer: Develops and tests AI prompts and behaviors; understands LLM capabilities and enterprise integration. Workforce & Business Operations Roles Human–AI Collaboration Lead: Defines frameworks for humans and AI to work together effectively; skilled in change management and strategy. Adoption Strategist: Aligns AI initiatives with business goals; drives workforce adoption and ethical implementation. Technical Roles Responsible Use AI Architect: Designs safeguards for ethical AI use; leads responsible ML initiatives. Orchestration Engineer: Connects and coordinates multiple AI agents, tools, and workflows with reliability and guardrails. AI Engineer: Builds scalable AI solutions; proficient in ML, data engineering, and coding (Python, SQL). AI Architect: Designs AI infrastructure including data pipelines, systems, and governance frameworks. Data Annotator: Labels and categorises raw data for AI training; requires scripting and LLM familiarity. Supervisory & Leadership Roles Head of AI: Leads company-wide AI strategy and innovation efforts. Agent Operations Manager: Oversees performance and maintenance of AI agents; manages incidents and model quality. SVP of AI Strategy: Shapes long-term AI direction and responsible technology adoption. EVP of AI (Walmart): Directs AI transformation and platform strategy to boost productivity and innovation.

The Agentic AI Developer Roadmap — From Core Skills to Cutting-edge AI Agentic AI is reshaping how we build, deploy, and scale intelligent systems. But for developers, the real question is — how do you navigate this rapidly evolving space and choose the right career path? I’ve put together this Agentic AI Developer Roadmap to help you map your journey: 🔹 Foundation (Core Skills) Before diving into specialized roles, every AI developer should master: Python & JavaScript essentials Prompt engineering fundamentals API & function calling basics Data handling (JSON, CSV, APIs) Git & version control AI model fine-tuning basics Deployment (Docker, cloud basics) 🔹 Career Paths in Agentic AI 1️⃣ Agent Workflow Developer – Logic & task orchestration with multi-agent setups, memory handling, and feedback loops. 2️⃣ AI Tool Integrator – Connecting AI to external systems, plugins, APIs, and real-time event pipelines. 3️⃣ AI Infrastructure Engineer – Scaling, monitoring, and optimizing AI models in production. 4️⃣ Multi-modal AI Developer – Beyond text: integrating vision, audio, and multi-format processing. 5️⃣ Specialized Domain Agent Creator – Industry-specific AI with compliance, regulation, and domain expertise. 6️⃣ Autonomous Agent Researcher – Pioneering self-improving architectures, reasoning systems, and safe AI design. Agentic AI isn’t just about building smarter models — it’s about designing AI that can reason, adapt, and take meaningful action. The opportunities span from enterprise-scale infrastructure to cutting-edge research, and this roadmap can help you find where you fit in. Which role do you see yourself growing into over the next 2 years in the Agentic AI space?

There are 4 ways to win in agentic AI. Most companies are trying to master them all, and McKinsey & Company says that's a mistake. McKinsey published a framework mapping out how tech services players will actually create value in the agentic era. It breaks down into 4 distinct roles, and I want you to notice the gap between them: 1-The Agentic AI Enabler These are the foundational layer players with broad domain understanding, deep engineering across cloud, data and orchestration, and strong enough infrastructure to let other companies build on top of them. It is not a position most companies can realistically claim. 2-The Packaged Agent Implementer This is where you need minimal domain knowledge. They take prebuilt agents and deploy them across client processes. Their value isn't in building, it’s in rollout speed and change management. This category might seem easier to enter, but they are harder to differentiate. 3-The Custom Agent Developer In this role, you get deep domain expertise plus privileged access to enterprise data with full-stack build capability. All niche AI talent like prompt engineers, orchestration specialists, and cocreation teams belong in this. They might have fewer clients, higher complexity, harder to scale, but much harder to replace. 4-The End-to-End Workflow Disruptor This is by far the most demanding position. In this role, you're not building agents but redesigning how the entire business functions operate around agents. It requires cross-functional squads, serious change management, and the ability to embed multi-agent systems into reimagined workflows. Agentic AI isn't a single market with a single winner.  Each position rewards a different capability mix. Positioning is a commitment companies haven't made yet. #agenticai #business #multiagent

By 2030, we’ll see 92 million jobs lost, and 𝟏𝟕𝟎 𝐦𝐢𝐥𝐥𝐢𝐨𝐧 jobs created, according to the latest WEF report. We’re heading toward a global churn of 22% of current jobs by then. And many of the new ones? They’re being shaped and accelerated by AI. Some of the fastest-growing roles globally, directly driven by AI adoption, include: - AI and Machine Learning Specialists - Big Data Analysts - AI-augmented UX Designers - Information Security Analysts - Fintech Engineers - Process Automation Specialists ... Many of these roles barely existed at scale just a few years ago. And they’re not all technical. We’re also seeing roles like prompt engineers, AI ethics leads, and AI product strategists gaining traction across different industries. 𝐎𝐧𝐞 𝐬𝐡𝐢𝐟𝐭 𝐭𝐡𝐚𝐭’𝐬 𝐛𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐦𝐨𝐫𝐞 𝐯𝐢𝐬𝐢𝐛𝐥𝐞 𝐧𝐨𝐰 𝐢𝐬 𝐭𝐡𝐞 𝐫𝐢𝐬𝐞 𝐨𝐟 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬. We’re moving from simple model outputs to systems that can take actions, use tools, and follow goals across multiple steps. That shift is bringing new types of roles with it: • Engineers and researchers building agent frameworks • Product teams defining how agents fit into user journeys • Decision engineers designing shared workflows between humans and machines • Governance and compliance leads ensuring safety and alignment ... And this shift isn’t limited to labs or big tech. Thanks to the growth of 𝐨𝐩𝐞𝐧-𝐬𝐨𝐮𝐫𝐜𝐞 𝐀𝐈 𝐭𝐨𝐨𝐥𝐬, agent development is becoming more accessible. And open source frameworks like LangChain are lowering the barrier for experimentation. 📍We also just open-sourced 𝐆𝐞𝐧𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐎𝐒, a lightweight framework we’ve been using internally to run multi-agent systems. If you’re playing around with agent workflows, feel free to check it out: GitHub: https://bit.ly/4kzE1Mt And if you’re into open source, a ⭐ would mean a lot! __________ For more on AI and Data Science, plz check my previous posts. I share my journey here. Join me and let's grow together. Alex Wang #technology #aiagents #agenticai #generativeai

Harvard Business Review just named a role most companies don't have yet: the AI Agent Manager. Not a data scientist. Not a prompt engineer. A manager who sets goals, delegates tasks, reviews output, and course-corrects. Except the team is made of AI agents. Gartner says 40% of enterprise applications will include task-specific AI agents by end of 2026. That's up from less than 5% today. Someone will need to manage them. I wrote about what that role looks like, the 5 skills it requires, and why the mid-managers who figure this out first will be the most valuable leaders in any organization over the next three years. #AI

Java Developers: AI Agents Aren’t Just a Python Story Enterprise AI is moving beyond chatbots toward agents that can reason, retrieve data, call APIs, and take controlled actions. Here’s an architecture I designed for building a production-ready AI Agent using: The flow: User → API Gateway → Spring AI Agent → Amazon Bedrock → Tools → Enterprise Data → Response The Spring AI Agent acts as the orchestration layer: → Understands user intent → Selects the right tools → Calls existing Java microservices → Retrieves enterprise knowledge using RAG → Maintains conversation context → Uses Bedrock models for reasoning → Returns an intelligent response Example: Customer asks: “Why is my $5,000 wire transfer still pending?” Instead of simply generating an answer, the agent can: Payment Service → Fraud/KYC → RAG Knowledge Base → Reasoning → Response For production systems, the key is keeping business rules, authorization, and high-risk actions inside deterministic services — not giving unrestricted control to the LLM. Add IAM, Guardrails, human approval, audit logs, observability, retries, and DLQs, and we have the foundation for an enterprise-grade Agentic AI platform. The opportunity for Java developers is huge. Java isn’t being replaced by AI. Java becomes the secure execution layer behind AI agents. 𝗞𝗲𝗲𝗽𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝗶𝗻 𝗺𝗶𝗻𝗱, 𝗜 𝘄𝗲𝗻𝘁 𝗱𝗲𝗲𝗽 𝗮𝗻𝗱 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗲𝗱 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗶𝗻𝘁𝗼 𝗮 𝗝𝗮𝘃𝗮 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗚𝘂𝗶𝗱𝗲. 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗴𝘂𝗶𝗱𝗲 𝗵𝗲𝗿𝗲: https://lnkd.in/dfhsJKMj #Java #AI #SpringBoot

𝐖𝐡𝐢𝐜𝐡 𝐀𝐈 𝐑𝐨𝐥𝐞𝐬 𝐖𝐢𝐥𝐥 𝐘𝐨𝐮𝐫 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐍𝐞𝐞𝐝 𝐢𝐧 𝐭𝐡𝐞 𝐍𝐞𝐱𝐭 𝐓𝐰𝐨 𝐘𝐞𝐚𝐫𝐬? The job titles you knew two years ago won't carry you through the next two. GenAI is rewriting org charts faster than HR can update job descriptions. 𝐖𝐡𝐞𝐫𝐞 𝐝𝐨 𝐭𝐡𝐞𝐬𝐞 𝐫𝐨𝐥𝐞𝐬 𝐟𝐢𝐭 𝐚𝐜𝐫𝐨𝐬𝐬 𝐜𝐚𝐫𝐞𝐞𝐫 𝐬𝐭𝐚𝐠𝐞𝐬? 1. Foundation: Build the infrastructure, data layers, and platforms everything else runs on. 2. Builder: Create and develop models, products, and applied solutions. 3. Specialist: Deepen expertise in narrow, high-value domains. 4. Strategist: Drive insights that connect AI capability to business outcomes. 5. Leader: Shape direction, governance, and org-wide AI bets. 𝐖𝐡𝐚𝐭 𝐫𝐨𝐥𝐞𝐬 𝐞𝐱𝐢𝐬𝐭 𝐚𝐭 𝐞𝐚𝐜𝐡 𝐬𝐭𝐚𝐠𝐞? Established AI Roles: • AI Architect: Designs end-to-end AI systems. • AI Product Manager: Owns roadmap and user value. • Data Scientist: Turns data into models and insight. • ML Engineer: Builds and ships production ML. • AI Developer: Embeds AI into real applications. These roles aren't new but the expectations inside them have changed dramatically with GenAI. 𝐄𝐦𝐞𝐫𝐠𝐢𝐧𝐠 𝐀𝐈 𝐑𝐨𝐥𝐞𝐬: • Prompt Engineer: Designs reliable model interactions. • Knowledge Engineer: Structures domain knowledge for retrieval and reasoning. • Model Validator: Tests model behavior, accuracy, and edge cases. 𝐌𝐮𝐬𝐭-𝐇𝐚𝐯𝐞 𝐀𝐈 𝐑𝐨𝐥𝐞𝐬: • AI Ethicist: Ensures responsible, fair, and safe deployment. • Governance Specialist: Owns policy, compliance, and risk frameworks. • Decision Engineer: Designs how humans and AI share decision-making. The must-have roles are the ones most orgs haven't hired yet and the ones regulators will ask about first. What capabilities should every AI role be measured on? The biggest gap in most orgs isn't talent it's role design.  Teams hire one "AI lead" and expect them to cover strategy, ethics, model ops, and product.  That model is breaking.  The orgs scaling AI are splitting these capabilities across deliberate, named roles pairing every Builder with a Validator, every Strategist with a Governance partner. If your org chart still treats AI as a single function, you're already behind. Which role is your organization missing today? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) for more PS: Found this useful? Join 2,500+ AI architects and engineering leaders from Microsoft, Google, IBM, PwC and others reading my weekly newsletter 𝗗𝗶𝗮𝗿𝘆 𝗼𝗳 𝗮𝗻 𝗔𝗜 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁. I break down real enterprise AI systems, agentic patterns, and what actually works in production. ✉️ Free subscription: https://lnkd.in/exc4upeq #GenAI #AICareer #FutureOfWork