Architect Artificial Intelligence/Machine Learning Engineer

Ciklum

About The Position

Ciklum is looking for a AI Architect/Machine Learning Engineer to join our team full-time in Canada. We are a custom product engineering company that supports both multinational organizations and scaling startups to solve their most complex business challenges. With a global team of over 4,000 highly skilled developers, consultants, analysts and product owners, we engineer technology that redefines industries and shapes the way people live. Project Description: We are seeking an AI Architect to lead the design of next-generation enterprise AI systems centered on Agentic AI, advanced RAG patterns, multimodal model integration, and secure orchestration frameworks. This role focuses entirely on model integration, inference optimization, safety, orchestration, document ingestion, evaluation, and architecture. The architect will drive standards for multi-agent orchestration, tool calling, governance, and design patterns across business and engineering teams.

Requirements

10+ years of professional experience in software or data engineering, including at least 4–5 years in AI/ML solution architecture or AI leadership roles
Demonstrated experience leading cross-functional AI programs or large-scale AI solution deliveries
BSc, MSc, or PhD in Computer Science, Mathematics, Engineering or a related quantitative field
Deep understanding of probability, statistics and the mathematical foundations of machine learning and optimization
Proven experience architecting and scaling enterprise-grade AI systems, including LLM-driven and multimodal solutions, across production environments
Experience defining AI system blueprints, reference architectures, and integration patterns across multiple domains and client ecosystems
Exposure to agentic system design, retrieval-augmented generation (RAG) and prompt engineering techniques
Strong proficiency in Python and common AI/ML development frameworks (e.g., PyTorch, TensorFlow, LangChain,Lang Graph, Hugging Face or equivalent)
Solid understanding of modern AI engineering practices, including model lifecycle management, observability, evaluation, versioning and continuous improvement
Familiarity with AI solution delivery methodologies (e.g., CRISP-ML(Q), TDSP or modern agile ML lifecycles)
Ability to visualize, interpret, and communicate model outputs and insights effectively using modern tools and dashboards
Proven experience in architecting and implementing end-to-end AI/ML solutions — from data ingestion and model training to deployment, monitoring and optimization
Strong software engineering skills for AI system development, including data processing, API integration, and model serving
Hands-on experience with cloud-native AI platforms and services (AWS SageMaker, Amazon Bedrock, Azure ML, GCP Vertex AI or NVIDIA AI stack)
Experience with diverse data modalities (structured, text, image, audio, video) and multimodal models
Understanding of security, data governance and compliance considerations in AI system design
Ability to advise on AI opportunity assessment, ROI modeling and transformation strategies
Experience designing AI governance frameworks and compliance strategies in highly regulated industries
Broad exposure to enterprise-scale AI solution design across industries such as BFSI, Healthcare, Aerospace, Manufacturing, Energy, Telecom or Technology sectors
Proven ability to translate business and operational requirements into robust AI system architectures that deliver measurable impact
Familiarity with challenges of deploying AI in regulated environments and ensuring compliance with data privacy and protection frameworks (e.g., GDPR, CCPA, PCI DSS)
Experience managing sensitive or high-value data (PII, PHI), implementing strong security, governance and access control mechanisms
Understanding of enterprise data ecosystems and integration patterns (CRM, ERP, knowledge management or workflow systems)
Strong executive communication and storytelling ability, capable of influencing technical and business audiences alike
Proven record of representing organizations in external speaking engagements, conferences, or public panels
Demonstrated experience driving innovation initiatives and contributing to organizational AI strategy
Proven experience delivering production-grade AI solutions that achieve measurable business and operational outcomes
Strong ownership of the full AI engineering lifecycle — from problem framing and architecture design to deployment, optimization, and continuous improvement
Ability to align technical decisions with business priorities, ensuring scalability, reliability, and measurable value from AI initiatives
Excellent collaboration and communication skills to work effectively with cross-functional stakeholders, delivery teams, and clients
High degree of autonomy, accountability, and attention to detail in managing complex, multi-component AI systems

Nice To Haves

Track record of AI thought leadership through publications, blogs, podcasts or open-source contributions
Recognized presence in professional or academic AI communities (e.g., conferences, research collaborations)
Experience building and scaling internal AI centers of excellence or practice communities
Strong background in software or solution architecture, ideally with previous experience as a Software or Data Architect
Proven ability to design scalable, distributed, and fault-tolerant AI architectures, integrating APIs, microservices, and event-driven components
Experience with MLOps and LLMOps practices, including pipeline automation, containerization (Docker, Kubernetes), and continuous deployment of AI models
Deep learning expertise using TensorFlow, PyTorch, or JAX, including fine-tuning and optimization of large models
Hands-on experience with Large Language Models (LLMs), Generative AI applications, and agentic or RAG-based systems
Advanced SQL and familiarity with modern data platforms (Databricks, Snowflake, or equivalent)
Experience with Big Data and streaming frameworks (Apache Spark, Kafka, Flink, etc.)
Understanding of NoSQL and graph databases (e.g., Cassandra, Neo4j) and their role in AI knowledge management
Experience with cloud-native architectures and certified expertise in AWS, Azure, or GCP AI/ML services
Exposure to research or innovation projects, with publications or open-source contributions considered an advantage

Responsibilities

Contribute defining and evolving Ciklum’s AI Engineering strategy, frameworks and best practices across clients and internal initiatives
Being able to manage senior stakeholders at Director Level or above levels
Understand vision and roadmap and develop AI solutions in accordance
Lead the design, development and deployment of advanced AI systems across Data Science and AI Engineering domains
Architect and lead implementation of scalable AI pipelines and LLM-driven applications, including retrieval-augmented generation (RAG)
Contribute hands-on development activities, from experimentation and prototyping to production-grade
Collaborate with cross-functional engineering, data and product teams to align technical solutions with clients and their business objectives
Apply MLOps and LLMOps best practices for CI/CD, observability, evaluation and continuous improvement of AI models and pipelines
Integrate AI services with enterprise platforms (e.g., Confluence, Jira, GitHub, CRM, ERP) and ensure seamless interoperability
Drive innovation through internal frameworks and accelerators
Ensure all AI solutions comply with security, data privacy and responsible-AI standards
Commercial / Presales: Drive technical leadership in presales engagements, shaping AI strategies and architectures that align with client business objective

Benefits

Strong community: Work alongside top professionals in a friendly, open-door environment
Growth focus: Take on large-scale projects with a global impact and expand your expertise
Tailored learning: Boost your skills with internal events (meetups, conferences, workshops), Udemy access, language courses, and company-paid certifications
Endless opportunities: Explore diverse domains through internal mobility, finding the best fit to gain hands-on experience with cutting-edge technologies
Care: Healthcare, Basic Life Insurance, Short and Long-term disability insurance according to the Company’s Benefit Plans

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