LLMOps Engineer Job at Thrive Career Wellness Inc (Toronto)

AI Engineer

Guidepoint seeks an experienced AI Engineer as an integral member of the Toronto...

Location

Canada , Toronto

Salary:

Not provided

Modoras Accounting Syd

Expiration Date

Until further notice

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related technical field with 6+ years of professional experience
or a Master’s degree with 4+ years of professional experience in backend software engineering and Generative AI
Proven track record of designing, building, and scaling distributed, production-grade systems
Deep expertise in Python, a major backend framework (e.g., FastAPI, Flask), and asynchronous programming (e.g., asyncio)
Proficiency in designing RESTful APIs, microservices, and the complete operational lifecycle, including comprehensive testing, CI/CD (e.g., ArgoCD), observability, monitoring, alerting, maintaining high uptime, and executing zero-downtime deployments
Hands-on experience deploying and managing applications on a major cloud platform (Azure preferred, AWS/GCP acceptable) using containerization (Docker) and orchestration (Kubernetes, Helm)
2+ years of experience building applications that leverage large language models from providers like OpenAI, Anthropic, or Google Gemini
Direct experience with modern LLM patterns such as retrieval-augmented generation (RAG), hybrid search using vector databases (e.g., Pinecone, Elasticsearch), multi-agent AI systems with tool calls, and prompt engineering
Experience designing and implementing robust evaluation frameworks for LLM-based systems, including rubric-based scoring, LLM Judges, or using tools like MLflow, alongside monitoring for performance and drift
Familiarity with large-scale data processing platforms and tools (e.g., Databricks, Apache Spark)

Job Responsibility

Architect and Build Production Systems: Design, build, and operate scalable, low-latency backend services and APIs that serve Generative AI features, from retrieval-augmented generation (RAG) pipelines to complex agentic systems
Own the AI Application Lifecycle: Own the end-to-end lifecycle of AI-powered applications, including system design, development, deployment (CI/CD), monitoring, and optimization in production environments like Databricks and Azure Kubernetes Service (AKS)
Optimize RAG Pipelines: Continuously improve retrieval and generation quality through techniques like retrieval optimization (tuning k-values, chunk sizes), using re-rankers, advanced chunking strategies, and prompt engineering for hallucination reduction
Integrate Intelligent Systems: Engineer solutions that seamlessly combine LLMs with our proprietary knowledge repositories, external APIs, and real-time data streams to create powerful copilots and research assistants
Champion LLMOps and Engineering Best Practices: Collaborate with data science and engineering teams to establish and implement best practices for LLMOps, including automated evaluation using frameworks like LLM Judges or MLflow, AI observability, and system monitoring
Evaluate and Implement AI Strategies: Systematically evaluate and apply advanced prompt engineering methods (e.g., Chain-of-Thought, ReAct) and other model interaction techniques to optimize the performance and safety of proprietary and open-source LLMs
Mentor and Lead: Provide technical leadership to junior engineers through rigorous code reviews, mentorship, and design discussions, helping to elevate the team's engineering standards
Influence the Roadmap: Partner closely with product and business stakeholders to translate user needs into technical requirements, define priorities, and shape the future of our AI product offerings

What we offer

Paid Time Off
Comprehensive benefits plan
Company RRSP Match
Development opportunities through the LinkedIn Learning platform

Data/AI Engineer

Guidepoint seeks an experienced Data/AI Engineer as an integral member of the To...

Location

Canada , Toronto

Salary:

Not provided

Modoras Accounting Syd

Expiration Date

Until further notice

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related technical field with 6+ years of professional experience
or a Master’s degree with 4+ years of professional experience in backend software engineering and Generative AI
Proven track record of designing, building, and scaling distributed, production-grade systems
Deep expertise in Python, a major backend framework (e.g., FastAPI, Flask), and asynchronous programming (e.g., asyncio)
Proficiency in designing RESTful APIs, microservices, and the complete operational lifecycle, including comprehensive testing, CI/CD (e.g., ArgoCD), observability, monitoring, alerting, maintaining high uptime, and executing zero-downtime deployments
Hands-on experience deploying and managing applications on a major cloud platform (Azure preferred, AWS/GCP acceptable) using containerization (Docker) and orchestration (Kubernetes, Helm)
2+ years of experience building applications that leverage large language models from providers like OpenAI, Anthropic, or Google Gemini
Direct experience with modern LLM patterns such as retrieval-augmented generation (RAG), hybrid search using vector databases (e.g., Pinecone, Elasticsearch), multi-agent AI systems with tool calls, and prompt engineering
Experience designing and implementing robust evaluation frameworks for LLM-based systems, including rubric-based scoring, LLM Judges, or using tools like MLflow, alongside monitoring for performance and drift
Familiarity with large-scale data processing platforms and tools (e.g., Databricks, Apache Spark)

Job Responsibility

Architect and Build Production Systems: Design, build, and operate scalable, low-latency backend services and APIs that serve Generative AI features, from retrieval-augmented generation (RAG) pipelines to complex agentic systems
Own the AI Application Lifecycle: Own the end-to-end lifecycle of AI-powered applications, including system design, development, deployment (CI/CD), monitoring, and optimization in production environments like Databricks and Azure Kubernetes Service (AKS)
Optimize RAG Pipelines: Continuously improve retrieval and generation quality through techniques like retrieval optimization (tuning k-values, chunk sizes), using re-rankers, advanced chunking strategies, and prompt engineering for hallucination reduction
Integrate Intelligent Systems: Engineer solutions that seamlessly combine LLMs with our proprietary knowledge repositories, external APIs, and real-time data streams to create powerful copilots and research assistants
Champion LLMOps and Engineering Best Practices: Collaborate with data science and engineering teams to establish and implement best practices for LLMOps, including automated evaluation using frameworks like LLM Judges or MLflow, AI observability, and system monitoring
Evaluate and Implement AI Strategies: Systematically evaluate and apply advanced prompt engineering methods (e.g., Chain-of-Thought, ReAct) and other model interaction techniques to optimize the performance and safety of proprietary and open-source LLMs
Mentor and Lead: Provide technical leadership to junior engineers through rigorous code reviews, mentorship, and design discussions, helping to elevate the team's engineering standards
Influence the Roadmap: Partner closely with product and business stakeholders to translate user needs into technical requirements, define priorities, and shape the future of our AI product offerings

What we offer

Paid Time Off
Comprehensive benefits plan
Company RRSP Match
Development opportunities through the LinkedIn Learning platform

Senior Machine Learning Engineer (Team Lead)

As our Artificial Intelligence (AI) and Machine Learning (ML) Team Leader, you w...

Location

Australia , South Bank

Salary:

Not provided

Flight Centre Brand

Expiration Date

Until further notice

Requirements

7+ years delivering production grade ML or AI systems with proven commercial impact
3+ years Leading and Mentoring engineers
Experience building AI agents, RAG systems or LLM powered applications in production
Demonstrated experience leading technical teams and managing complex AI programmes
Strong hands on experience across ML infrastructure, distributed systems and scalable AI architecture
Experience building and governing AI agent platforms including endpoints, gateways and tool orchestration
Familiarity with MCP servers and emerging agent communication standards and protocols
Experience defining evaluation frameworks, safety mechanisms and governance for LLM and agent based systems
Deep knowledge of Python, modern AI/ML frameworks and scalable AI platforms including Databricks
Strong expertise in Kubernetes and cloud native production environments

Job Responsibility

Lead the development and productionisation of ML models, LLM powered systems and agent based applications
Define and build end to end MLOps including CI CD, model registry, monitoring, drift detection and retraining for predictive ML systems
Establish LLMOps standards including context engineering, automated evaluation pipelines, red teaming, safeguards and policy guardrails
Architect and build AI agent workflows, endpoints, gateways and orchestration layers enabling secure tool access, structured reasoning and multi agent collaboration
Design and govern MCP servers and modern agent communication protocols to ensure interoperability, security and scalability
Implement strong observability across ML and GenAI systems including reliability, latency, evaluation metrics, usage tracking and cost control
Drive scalable ML infrastructure, feature stores and data platforms on Databricks
Oversee Kubernetes based deployments and cloud native AI infrastructure
Partner with senior stakeholders to prioritise and deliver multiple high impact AI initiatives
Coach and grow a high performing AI engineering team

What we offer

Individualised, ongoing Learning & Development via communities of practice
Innovation Days
Dedicated Engineering Days
Access to 'LinkedIn Learning' for ongoing skills development
Women in PM&E group
Exclusive Staff Discounts
Travel Discounts
Career opportunities in a network of brands and businesses across the globe
Corporate Health Discounts
Mental Health Support and Employee Assistance Program for staff and family

Fulltime

Senior LLMOps Engineer

Working closely with our Engineering Manager, you’ll be a Senior LLMOps Engineer...

Location

Australia , Sydney

Salary:

Not provided

Heidi

Expiration Date

Until further notice

Requirements

Proven track record of designing, building, and maintaining MLOps or LLMOps infrastructure in a production environment
Previous hands-on experience building scalable, cloud-native infrastructure and platforms
Deployed and managed large-scale machine learning models in a production environment
Expert in Python, cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and Infrastructure as Code (e.g., Terraform, CloudFormation)
Deep and practical understanding of the entire machine learning lifecycle and the specific operational challenges of large language models
Ability to translate complex engineering and research requirements into concrete, robust, and automated platform solutions
Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience

Job Responsibility

Lead the architecture, design, and implementation of our end-to-end LLMOps platform, from data ingestion and model training pipelines to production deployment and monitoring
Build and maintain robust CI/CD/CT (Continuous Integration/Continuous Delivery/Continuous Training) pipelines to automate the testing, validation, and deployment of large language models
Engineer highly available and scalable model serving solutions using modern infrastructure like Kubernetes, ensuring low latency and high throughput for our production services
Collaborate closely with AI research and engineering teams to understand their needs, streamline workflows, and create the tooling that accelerates their development cycles
Champion and implement best practices for model versioning, experiment tracking, monitoring, and governance across the organization
Mentor mid-level and junior engineers, sharing your deep expertise in infrastructure, automation, and operational excellence to foster a culture of reliability and scalability

What we offer

Flexible hybrid working environment, with 3 days in the office
Additional paid day off for your birthday and wellness days
Special corporate rates at Anytime Fitness in Melbourne, Sydney tbc
A generous personal development budget of $500 per annum
Learn from some of the best engineers and creatives, joining a diverse team
Become an owner, with shares (equity) in the company

Fulltime

Senior Data & Applied Scientist

The AI Engineering team within MDO develops and deploys leading AI experiences t...

Location

United States , Redmond

Salary:

119800.00 - 234700.00 USD / Year

Microsoft Corporation

Expiration Date

Until further notice

Requirements

Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience
6+ years of professional experience delivering production AI/ML systems, including end-to-end model development, deployment, and monitoring
Hands-on experience with Azure-native platforms: Fabric, Kusto, Databricks, Synapse, Azure ML Studio
Background in supply chain, manufacturing, or hardware operations - understanding of demand planning, yield optimization, or logistics is a strong plus
Experience building and maintaining MLOps infrastructure: CI/CD for models, automated retraining, drift detection, and rollout strategies
Strong systems thinking — able to make pragmatic trade-offs between model performance, engineering cost, and operational simplicity
Experience with optimization methods (linear/mixed-integer programming, simulation) applied to operational problems
Strong proficiency in Python and SQL
hands-on experience with cloud ML platforms (Azure ML, Databricks, or equivalent)
Experience with GenAI/LLM systems in production: RAG pipelines, prompt engineering, fine-tuning, or LLMOps (model versioning, evaluation, monitoring)

Job Responsibility

Partner with business stakeholders, Solution Managers, TPMs, and engineering teams to deeply understand business context, identify high-value AI opportunities, and design intelligent solutions that deliver measurable business outcomes
Frame complex operational problems into well-defined analytical and technical approaches, bridging business needs with production-ready AI capabilities
Architect and build production-grade AI systems (ML/DL, GenAI, optimization) using Azure AI, open-source, and custom models with full ownership from prototype to deployment
Drive engineering excellence: establish standards for code quality, MLOps, observability, testing, and secure deployment across the team
Evaluate emerging AI tools/frameworks
advise on adoption decisions
Represent MDO as a technical SME in cross-Microsoft AI initiatives
Mentor and coach team members, raising the technical bar and fostering rigorous, inclusive innovation
Embody our Culture and Values

Fulltime

LLM Engineer

You will join our global Machine Learning and Data Science unit — a core team of...

Location

Spain , Barcelona

Salary:

Not provided

Gipo

Expiration Date

Until further notice

Requirements

At least one year of professional experience in LLM development or integration in a fast-paced, product-driven tech environment
Demonstrated expertise in production-grade LLM deployments, including prompt management systems, vector databases, semantic search implementation, and API integration with foundation models
Good understanding of transformer architectures and proficiency in LLM frameworks such as LangChain, LlamaIndex, or similar tools
Proficiency in Python
Experience in collaborative project development
Appreciation for good engineering practices and maintainable code
Proven experience in evaluating LLMs through systematic testing, benchmark design, and the development of custom metrics (e.g. accuracy, consistency, factuality, and bias), with a focus on aligning results to product and user needs
Proven ability to integrate, deploy, and optimize large language models in production-grade industry environments, ensuring scalability and robust performance
Strong knowledge in prompt engineering, agent-based workflows, and the generation and manipulation of embeddings
Experience with RAG (Retrieval-Augmented Generation) techniques, vector similarity search, and information retrieval methods to enhance LLM capabilities

Job Responsibility

Work closely with cross-functional teams, including scientists, engineers, and product stakeholders, to deliver LLM-driven initiatives that directly contribute to business objectives
Design, deploy and iterate over LLM services for text-based applications (and beyond), while proactively identifying and eliminating performance bottlenecks
Build small to medium-sized Python projects and collaborate with engineers on production code and deployments at scale
Assess platform engineering and LLMOps bottlenecks
research and design scalable prompt management strategies, and recommend solutions that balance performance, cost, and reliability
Research, architect, and deploy LLM-powered information retrieval solutions (e.g., RAG) to deliver accurate results in complex, multilingual product environments
Partner with the AI Platform team to refine LLMOps best practices, evolve frameworks, and establish efficient, scalable workflows

What we offer

Flexible remuneration and benefits system via Flexoh, which includes: restaurant card, transportation card, kindergarten, and training tax savings
Share options plan after 6 months of working with us
Remote or hybrid work model with our hub in Barcelona
Flexible working hours (fully flexible, as in most cases you only have to be on a couple of meetings weekly)
Summer intensive schedule during July and August (work 7 hours, finish earlier)
23 paid holidays, with exchangeable local bank holidays
Additional paid holiday on your birthday or work anniversary (you choose what you want to celebrate)
Private healthcare plan with Adeslas for you and subsidized for your family (medical and dental)
Access to hundreds of gyms for a symbolic fee in partnership for you and your family with Wellhub
Access to iFeel, a technological platform for mental wellness offering online psychological support and counseling

Fulltime

LLM Engineer

You will join our global Machine Learning and Data Science unit — a core team of...

Location

Poland , Warsaw

Salary:

Not provided

Gipo

Expiration Date

Until further notice

Requirements

At least one year of professional experience in LLM development or integration in a fast-paced, product-driven tech environment
Demonstrated expertise in production-grade LLM deployments, including prompt management systems, vector databases, semantic search implementation, and API integration with foundation models
Good understanding of transformer architectures and proficiency in LLM frameworks such as LangChain, LlamaIndex, or similar tools
Proficiency in Python
Experience in collaborative project development
Appreciation for good engineering practices and maintainable code
Proven experience in evaluating LLMs through systematic testing, benchmark design, and the development of custom metrics (e.g. accuracy, consistency, factuality, and bias), with a focus on aligning results to product and user needs
Proven ability to integrate, deploy, and optimize large language models in production-grade industry environments, ensuring scalability and robust performance
Strong knowledge in prompt engineering, agent-based workflows, and the generation and manipulation of embeddings
Experience with RAG (Retrieval-Augmented Generation) techniques, vector similarity search, and information retrieval methods to enhance LLM capabilities

Job Responsibility

Work closely with cross-functional teams, including scientists, engineers, and product stakeholders, to deliver LLM-driven initiatives that directly contribute to business objectives
Design, deploy and iterate over LLM services for text-based applications (and beyond), while proactively identifying and eliminating performance bottlenecks
Build small to medium-sized Python projects and collaborate with engineers on production code and deployments at scale
Assess platform engineering and LLMOps bottlenecks
research and design scalable prompt management strategies, and recommend solutions that balance performance, cost, and reliability
Research, architect, and deploy LLM-powered information retrieval solutions (e.g., RAG) to deliver accurate results in complex, multilingual product environments
Partner with the AI Platform team to refine LLMOps best practices, evolve frameworks, and establish efficient, scalable workflows

What we offer

Share options plan after 6 months of working with us
Remote or hybrid work model with or hub in Warsaw
Flexible working hours (fully flexible, as in most cases you only have to be on a couple of meetings weekly)
20/26 days of paid time off (depending on your contract)
Additional paid day off on your birthday or work anniversary (you choose what you want to celebrate)
Private healthcare plan with Signal Iduna for you and subsidized for your family
Multisport card co-financing for you to have access to sports facilities across Poland
Access to iFeel, a technological platform for mental wellness offering online psychological support and counseling
Free English classes

Fulltime

AI & Machine learning Engineer

We’re hiring an AI & Machine Learning Engineer to help design and deliver next‑g...

Location

Spain , Barcelona

Salary:

Not provided

FSP

Expiration Date

Until further notice

Requirements

3+ years of experience in machine learning, AI, data science, or software development, with recent focus on GenAI and LLMs
Experience with GenAI frameworks (e.g. Azure Foundry, CrewAI and Hugging Face)
Proficient in context engineering, RAG, and LLMOps
Experience deploying ML/AI solutions on Azure (Azure OpenAI, Azure AI Foundry, Azure ML Studio)
Experience with Azure data and analytics services (Data Factory, Data Lake, Synapse Analytics, SQL Database)
Programming skills in Python, R, or similar languages
Familiarity with ML frameworks and libraries (TensorFlow, PyTorch, Scikit-learn)
Experience with Azure DevOps, GitHub, or similar tools
Experience in Computer Vision for Optical Character Recognition (OCR) and object recognition

Job Responsibility

Deploy, fine-tune and monitor Generative AI models and Agentic AI for enterprise use cases
Develop and implement Retrieval-Augmented Generation (RAG) pipelines and advanced context engineering strategies
Integrate Agentic AI into business workflows
Collaborate with data engineers to bring Agentic capabilities to production
Stay current with AI trends, tools, and best practices, and drive innovation within the team

What we offer

A collaborative and supportive environment in which you can grow and develop your career
The tools and opportunity to do work you can be proud of
A chance to work alongside some of the best people in the industry, who always seek to share their knowledge and experience
Hybrid working – we empower you to make smart choices about when and where to work to achieve great results
Industry leading coaching and mentoring
Competitive salary and an excellent benefits package

Fulltime

LLMOps Engineer

Thrive Career Wellness Inc

Location:
Canada , Toronto

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Job Description:

Job Responsibility:

Requirements:

Nice to have:

Additional Information:

Job Posted:
February 24, 2026

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