• Provide strategic leadership and direction to 2 to 3 small, senior-caliber engineering teams • Guide teams in understanding client needs, setting project goals, and ensuring successful implementation • Manage 2–3 high-performing implementation teams of senior engineers • Support the company’s rapid growth as we double our client base • Drive delivery against the product roadmap • Champion collaborations with TA and existing external sourcing vendors • Refine onboarding methodologies • Engage with organizational leaders to co-design and champion career progression frameworks • Mentor an Engineering Talent Manager • Dive deeper into analytics and connect the dots between talent metrics and broader business objectives • Champion an AI-first engineering culture • Facilitate the creation and management of data and information repositories • Foster an environment of proactive domain-specific knowledge sharing among team members • Pioneer refinements in delivery processes • Advocate for and pursue expertise in advanced talent management strategies • Maintain regular and reliable attendance

• Lead engineering delivery for agentic AI capabilities for GTM stakeholders across its technology stack(Salesforce, Slack, 3rd party apps) and In-House Platforms. • Design and build LLM-powered workflows, autonomous agents, multi-agent systems and AI-enhanced integrations using Agentcore, Slack, MCPs, Langchain. • Define scalable enterprise AI architecture patterns including model routing, orchestration, memory management and governance strategies. • Design and optimize Retrieval Augmented Generation(RAG) systems, semantic search pipelines, vector retrieval strategies and enterprise knowledge grounding frameworks. • Build and maintain Apex, Lightning Web Components, Salesforce Platform Events, and Agentforce agent actions. • Implement AI evaluation frameworks including prompt quality benchmarking, hallucination reduction and model performance monitoring. • Mentor senior and mid-level engineers; conduct code reviews and enforce AI engineering standards. • Define error handling, fallback strategies, and graceful degradation patterns for non-deterministic AI systems. • Retire legacy integrations and replace them with modern, agentic, event-driven patterns. • Champion security-first AI engineering: input validation, output sanitization, and prompt injection hardening. • Architect and implement CI/CD pipelines, deployment automation, and platform observability. • Embrace Agentic AI and LLM-powered tooling as a core part of your engineering practice. • Stay current with rapidly evolving AI/ML technologies and apply them pragmatically to GTM systems. • Champion automation-first thinking — if it can be agentic, make it agentic. • Build internal tools and apps to streamline data access and GTM decision-making. • Implement data governance and monitoring frameworks to enhance GTM system quality. • Evaluate vendors and AI platforms with a strategic build vs. buy mindset. • Identify and automate manual processes to increase organizational leverage.

• Lead engineering delivery for agentic AI capabilities for GTM stakeholders across its technology stack(Salesforce, Slack, 3rd party apps) and In-House Platforms. • Design and build LLM-powered workflows, autonomous agents, multi-agent systems and AI-enhanced integrations using Agentcore, Slack, MCPs, Langchain. • Define scalable enterprise AI architecture patterns including model routing, orchestration, memory management and governance strategies. • Design and optimize Retrieval Augmented Generation(RAG) systems, semantic search pipelines, vector retrieval strategies and enterprise knowledge grounding frameworks. • Build and maintain Apex, Lightning Web Components, Salesforce Platform Events, and Agentforce agent actions. • Implement AI evaluation frameworks including prompt quality benchmarking, hallucination reduction and model performance monitoring. • Mentor senior and mid-level engineers; conduct code reviews and enforce AI engineering standards. • Define error handling, fallback strategies, and graceful degradation patterns for non-deterministic AI systems. • Retire legacy integrations and replace them with modern, agentic, event-driven patterns. • Champion security-first AI engineering: input validation, output sanitization, and prompt injection hardening. • Architect and implement CI/CD pipelines, deployment automation, and platform observability. • Embrace Agentic AI and LLM-powered tooling as a core part of your engineering practice. • Stay current with rapidly evolving AI/ML technologies and apply them pragmatically to GTM systems. • Champion automation-first thinking — if it can be agentic, make it agentic. • Build internal tools and apps to streamline data access and GTM decision-making. • Implement data governance and monitoring frameworks to enhance GTM system quality. • Evaluate vendors and AI platforms with a strategic build vs. buy mindset. • Identify and automate manual processes to increase organizational leverage.

• Write and ship production AI code daily — you are an active contributor. • Architect agentic AI workflows using LangGraph, Temporal, Pydantic — stateful, multi-agent workflows built for enterprise scale and reliability. • Own AI observability via LangFuse: tracing, prompt versioning, evaluation, and performance benchmarking across all model interactions. • Set AI engineering standards for agent design patterns, RAG, prompt management, context optimization, and tool-calling strategies. • Partner with product and platform teams to deliver AI architectures that meet enterprise SLA, security, and compliance requirements. • Evaluate and adopt emerging tooling — benchmarking LLM providers, orchestration frameworks, and agentic stack improvements. • Mentor engineers as a natural extension of your work — sharing knowledge through code reviews, pairing sessions, and design discussions, not through management overhead. • Represent Acquia's AI capabilities in customer architectural reviews, technical discovery, and roadmap conversations.