This position is part of the Rotary and Mission Systems business area, where employees across Canada support engineering, systems integration, training, sustainment and in-service support programs for Canadian and international military customers across land, air and sea. Specific Job Description: Strategic Vision & Roadmap – Define and execute an end to end AI vision that aligns with business goals and regulatory requirements Use Case Prioritization & Model Selection – Evaluate potential AI applications based on business value, technical feasibility, ROI; recommend investments and technology adoptions to craft a prioritized roadmap Performance Monitoring – Continuously evaluate AI effectiveness, adjust strategies, and refine the roadmap using measurable performance metrics. Set KPIs that evaluate profitability, accuracy, and speed of deployed AI systems Cross functional Alignment – Partner with product, data engineering, business, and senior leaders to translate customer problems into AI driven solutions Requirements & Design – Work with product line managers and engineers to capture needs, create efficient software designs, and select appropriate models Stakeholder Engagement – Coordinate with data scientists, analysts, and business stakeholders to design, test, and roll out AI systems Leadership & Mentorship – Lead collaboration efforts, mentor junior members, and nurture a culture of innovation and continuous improvement MLOps & Operations – Build and maintain MLOps pipelines that support data preparation, experimentation, training, evaluation, model versioning, serving, monitoring, and continuous improvement. Implement best practices for deployment, monitoring (observability, guardrails, evaluation), security, and compliance Innovation – Stay current with AI advancements, computer vision, generative AI, LLM/VLM architectures, applied machine learning, research emerging technologies, and prototype new patterns Technical Leadership – Lead feasibility studies, proofs of concept, and R&D for emerging technologies. Evaluate and integrate cutting edge tools, frameworks, and cloud native solutions (AWS, GCP, Azure) Governance & Risk Management : Establishing frameworks for responsible and ethical AI, addressing security, data privacy, governance standards, bias, transparency, and regulatory compliance mitigating risks like hallucinations or algorithmic bias Architecture & Developmen t : Architect and build robust, scalable AI/ML systems, often involving Gen AI (LLMs, RAG, agents) or traditional models, using cloud platforms (AWS, Azure, GCP) Required skills, qualifications and experience: Bachelor’s or Master’s degree in Computer Science, Data Science, Information Technology, or a closely related field Ten or more years of professional experience in data science, AI/ML engineering, high level architectural design, and large scale AI/ML solution development, with at least five years in a technical leadership or management capacity Proven experience in strategy development—particularly around AI/ML and digital transformation—and a track record of productionizing models at scale Demonstrated expertise implementing MLOps practices (data pipelines, experimentation, serving, monitoring, retraining) Hands on experience building solutions for cloud environments (AWS preferred) or for edge/embedded systems (Ubuntu, AI accelerators, device hardware), with a deep understanding of performance trade offs Strong software engineering fundamentals, capable of delivering scalable, production ready ML systems Excellent communication skills and the ability to explain complex ML concepts clearly to both technical and non technical audiences Technical proficiency in Python, ML frameworks, MLOps tools, cloud platforms (Azure, AWS, GCP), data pipelines, vector databases, LLMs, and NLP Extensive experience with deep learning frameworks, time series analysis, predictive modeling, and NLP Demonstrated ability to evaluate third party LLM/VLM and AI services and to make well grounded recommendations Desired skills, qualifications and experience: Experience with computer vision, LLMs, multimodal models, and applied AI in real‑world contexts Exposure to video data pipelines: ingestion, streaming, storage, indexing, and analytics at scale Experience delivering ML‑powered features within cross‑functional Agile teams Experience implementing agentic AI workflows or similar orchestration patterns Familiarity with SageMaker, Flyte, Bedrock, or other cloud‑based AI frameworks Expertise in predictive analytics, deep learning architectures, and text analytics (NLP) Proficiency in Python for AI/ML; experience with graph databases, neural networks, and MLOps Knowledge of high‑performance AI computing platforms, Linux, and shell scripting in AI computing environments Familiarity with cloud services – Azure (preferred), AWS, GCP #J-18808-Ljbffr