Job opportunity
Senior AI Developer
UniSysTech Consulting
We are seeking an experienced
Senior AI Developer
to lead the design, development, and deployment of enterprise-grade Artificial Intelligence and Machine Learning solutions. In this role, you will focus on building intelligent back-end systems, automated workflows, and scalable AI capabilities.
While experience with cloud automation in AWS is highly valuable,
deep AI/ML expertise is the primary priority for this role.
We are looking for an engineer who excels at utilizing modern AI frameworks, LLMs, and intelligent automation to solve complex back-end problems.
Key Responsibilities
AI & Machine Learning Engineering:
Lead the design and implementation of production-ready AI/ML capabilities, including Generative AI models, Retrieval-Augmented Generation (RAG) architectures, and predictive solutions.
Intelligent Automations:
Build automated back-end pipelines and processes that leverage AI to optimize operational workflows, data processing, and enterprise tooling.
AWS Integration & Cloud Infrastructure:
Work closely with AWS services (e.g., SageMaker, Bedrock, Lambda, EventBridge) to host, automate, and scale AI-driven backend services.
Back-End API & Service Development:
Architect robust, secure, and performant back-end APIs, microservices, and asynchronous event-driven systems.
MLOps & Pipeline Automation:
Implement automated CI/CD workflows for model deployment, monitoring, model drift tracking, and continuous integration/delivery.
Technical Leadership & Architecture:
Partner with enterprise architects, data engineers, and security teams to ensure AI systems align with governance, compliance, and enterprise risk standards.
Qualifications & Key Requirements
Must-Have Skills (Top Priority)
Advanced AI / ML Expertise:
5+ years of hands-on experience building, training, fine-tuning, and deploying machine learning models and AI applications into production.
Generative AI & Modern AI Frameworks:
Proficiency with LLM orchestration (e.g., LangChain, LlamaIndex), RAG pipeline design, vector databases, and prompt engineering.
Back-End Mastery:
Strong proficiency in
Python
(FastAPI, Flask, or async Python frameworks) and software design patterns focused entirely on backend services, APIs, and data processing.
Automation Engineering:
Demonstrated experience automating complex backend tasks, data pipelines, and system workflows using AI-driven approaches.
AWS & Cloud Skills (Secondary Focus)
AWS Cloud Services:
Practical experience with core AWS backend and AI infrastructure, such as AWS Bedrock, SageMaker, Lambda, API Gateway, S3, ECS, and EventBridge.
Infrastructure as Code & DevOps:
Exposure to CloudFormation or Terraform, alongside standard Git-based CI/CD pipelines.
Nice-to-Have Skills
Background in large enterprise or regulated industries (e.g., Financial Services, Telecommunications).
Knowledge of Responsible AI principles, model governance, and data security standards.
Familiarity with containerization (Docker, Kubernetes/EKS).
Typescript, React, Angular and Java
Hybrid Model:
Requirement to work
2 days per week in-office
at our Downtown Toronto office, with up to 3 days remote flexibility.
#J-18808-Ljbffr
Apply on source site Senior AI Developer
to lead the design, development, and deployment of enterprise-grade Artificial Intelligence and Machine Learning solutions. In this role, you will focus on building intelligent back-end systems, automated workflows, and scalable AI capabilities.
While experience with cloud automation in AWS is highly valuable,
deep AI/ML expertise is the primary priority for this role.
We are looking for an engineer who excels at utilizing modern AI frameworks, LLMs, and intelligent automation to solve complex back-end problems.
Key Responsibilities
AI & Machine Learning Engineering:
Lead the design and implementation of production-ready AI/ML capabilities, including Generative AI models, Retrieval-Augmented Generation (RAG) architectures, and predictive solutions.
Intelligent Automations:
Build automated back-end pipelines and processes that leverage AI to optimize operational workflows, data processing, and enterprise tooling.
AWS Integration & Cloud Infrastructure:
Work closely with AWS services (e.g., SageMaker, Bedrock, Lambda, EventBridge) to host, automate, and scale AI-driven backend services.
Back-End API & Service Development:
Architect robust, secure, and performant back-end APIs, microservices, and asynchronous event-driven systems.
MLOps & Pipeline Automation:
Implement automated CI/CD workflows for model deployment, monitoring, model drift tracking, and continuous integration/delivery.
Technical Leadership & Architecture:
Partner with enterprise architects, data engineers, and security teams to ensure AI systems align with governance, compliance, and enterprise risk standards.
Qualifications & Key Requirements
Must-Have Skills (Top Priority)
Advanced AI / ML Expertise:
5+ years of hands-on experience building, training, fine-tuning, and deploying machine learning models and AI applications into production.
Generative AI & Modern AI Frameworks:
Proficiency with LLM orchestration (e.g., LangChain, LlamaIndex), RAG pipeline design, vector databases, and prompt engineering.
Back-End Mastery:
Strong proficiency in
Python
(FastAPI, Flask, or async Python frameworks) and software design patterns focused entirely on backend services, APIs, and data processing.
Automation Engineering:
Demonstrated experience automating complex backend tasks, data pipelines, and system workflows using AI-driven approaches.
AWS & Cloud Skills (Secondary Focus)
AWS Cloud Services:
Practical experience with core AWS backend and AI infrastructure, such as AWS Bedrock, SageMaker, Lambda, API Gateway, S3, ECS, and EventBridge.
Infrastructure as Code & DevOps:
Exposure to CloudFormation or Terraform, alongside standard Git-based CI/CD pipelines.
Nice-to-Have Skills
Background in large enterprise or regulated industries (e.g., Financial Services, Telecommunications).
Knowledge of Responsible AI principles, model governance, and data security standards.
Familiarity with containerization (Docker, Kubernetes/EKS).
Typescript, React, Angular and Java
Hybrid Model:
Requirement to work
2 days per week in-office
at our Downtown Toronto office, with up to 3 days remote flexibility.
#J-18808-Ljbffr