AI Engineering Manager

As AI Engineering Manager you will combine deep, hands-on AI engineering with people leadership, owning the technical architecture and engineering delivery, and leading and mentoring the engineers who build it.\nYou will architect RAG systems and production, multi-agent AI systems that plan tasks, call tools and APIs, and orchestrate real workflows. You will own agent and model design, evaluation, deployment, and monitoring, translate AARC's AI direction into robust technical architecture and delivery, and partner with data engineers, the cloud and data platform team, and business experts to ship reliable, trustworthy AI.\n\nWhat You Will Do\nTechnical Architecture & Engineering Strategy\nContribute to the technical and solution architecture for AARC's AI products: agentic systems, RAG, document intelligence, computer vision, and intelligent automation.\nContribute to AARC's AI strategy and roadmap, and translate AI direction into clear technical plans, milestones, and standards.\nMake the key architecture decisions: multi-agent patterns, retrieval design, model selection, fine-tuning vs. prompting, evaluation etc.\nSet engineering standards and reusable patterns for RAG, vector databases, prompt and tool-use orchestration, guardrails, and evaluation.\nHands-On AI Engineering (senior level)\nDesign autonomous and human-in-the-loop AI agents that plan, reason, and execute multi-step tasks (task decomposition, routing, reflection, retry/rollback).\nBuild multi-agent patterns (planner/solver/critic, researcher/writer/reviewer) using frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, or custom state machines.\nImplement tool-use/function-calling so agents can query databases, retrieve documents, call APIs, trigger workflows, and return structured outputs.\nArchitect end-to-end RAG pipelines: vector-database design (e.g., FAISS, pgvector, Milvus), ingestion (chunking, embeddings, hybrid search), and citations/grounding over documents such as reports, permits, and contracts.\nLead LLM fine-tuning and optimization using parameter-efficient methods (QLoRA, PEFT, LoRA) where it adds value.\nComputer Vision & Spatial AI\nBuild computer vision capabilities for imagery and document use cases: object detection, segmentation, classification, and OCR over photos, drone/aerial imagery, and scanned records.\nDevelop models and pipelines for LiDAR and point-cloud data, supporting site, terrain, infrastructure, and environmental use cases (e.g., feature extraction, measurement, and change detection).\nIntegrate vision and spatial outputs into agentic workflows and reporting alongside language models.\nProductionization, Evaluation & Observability\nShip agents and models behind secure APIs; implement versioning, canary and A/B rollouts, and automated regression tests.\nSet up agent telemetry (traces, spans, tool-latency, token/cost), drift and outlier alerts, and safety rails.\nDefine evaluation suites (task success, factuality/grounding, latency, cost, user satisfaction) and drive continuous improvement.\nAdd guardrails and policies (PII masking, rate limits, cost/latency budgets, escalation) suitable for secure, compliance-aware environments.\nPeople Management\nManage, mentor, and grow AARC's AI engineering team; engineering standards, code reviews, and career development.\nAct as a trusted technical advisor to leadership, translating business objectives and AI direction into technical plans and solutions.\nLead knowledge-sharing sessions and document patterns and best practices.\nProactively raise risks, blockers, dependencies, and timelines; communicate technical tradeoffs clearly to technical and executive audiences.\nCollaboration\nPartner with data engineers, the cloud and data platform team, full-stack engineers, and business and subject-matter experts to turn requirements into robust AI solutions.\nCommunicate complex AI concepts clearly to non-technical stakeholders.\n\nRequired Qualifications\nBachelor's or Master's degree in Computer Science, AI/ML, or a related field, or equivalent practical experience.\n8+ years in AI/ML or software engineering, including substantial hands-on experience building production AI/LLM systems, and 4+ years managing or leading engineers.\nStrong programming ability (especially Python) and familiarity with ML frameworks such as PyTorch or TensorFlow and LLM orchestration frameworks such as LangChain, or comparable.\nHands-on experience building RAG systems and/or LLM-powered agents in production.\nExperience with computer vision (e.g., object detection, segmentation, classification, or OCR) and the deep-learning frameworks behind it.\nDepth in at least one core area (advanced RAG, multi-agent orchestration, or LLM fine-tuning/adaptation such as QLoRA, PEFT, LoRA), with the ability to lead across all of them.\nExperience with vector databases (Pinecone, Azure AI or similar) and document processing (PDF parsing / OCR) at scale.\nExperience integrating AI with tools and APIs via function-calling / tool-use (databases, enterprise systems, internal services).\nExperience owning AI and technical/solution architecture for a product or platform.\nPeople-management experience: hiring, mentoring, setting standards, and developing engineers.\nClear communicator able to present complex AI concepts to non-technical and executive stakeholders.\n\nPreferred Qualifications\nDeep experience with multi-agent frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel) and/or custom agent state machines.\nProven track record fine-tuning LLMs (QLoRA / PEFT / LoRA).\nExperience with LiDAR or point-cloud processing, photogrammetry, or geospatial / GIS data.\nExperience with LLMOps / agent observability, evaluation frameworks, and prompt management.\nExperience deploying AI workloads across cloud providers (Azure OpenAI, AWS Bedrock, GCP Vertex AI) and with secure API deployment.\nFamiliarity with Microsoft Azure / Fabric, Power BI, and enterprise integrations (Monday.com, JotForm, FileMaker, SharePoint/Graph).\nRelevant certifications such as AI-102 or equivalent.\nExperience in regulated or compliance-driven industries: environmental, engineering, finance, healthcare, or legal.\n\nGrowth Opportunity\nThis role offers ownership of AARC's AI technical architecture and engineering delivery, and leadership of the AI engineering team.

You will set the engineering standards AARC builds on and help scale production AI and automation across the Group's businesses, with a clear path to grow the function as AARC takes on AI solution work for external clients.