AI Engineer
BiorenderSalary not specified
Full Time
Mid-level (3 to 4 years)
Not SpecifiedCompensation
Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level
Full TimeJob Type
UnknownVisa
Technology, DataIndustries
Requirements
- Bachelor’s or Master’s degree in Computer Science, Data Science, or related field
- Proficiency in Python or Node.js, with hands-on experience in FastAPI, LangChain, or spaCy
- Strong understanding of OCR, NLP, semantic search, and LLM-based automation
- Experience deploying solutions on AWS, GCP, or containerized (Docker/Kubernetes) environments
- Familiarity with vector databases (e.g., Pinecone, FAISS, Weaviate) and prompt orchestration frameworks
- Strong problem-solving skills and ability to translate business needs into scalable technical solutions
- Nice to Have: Experience with MLOps, Vertex AI Pipelines, or AWS SageMaker
- Nice to Have: Exposure to financial or document-intensive automation systems
- Nice to Have: Understanding of data drift detection, retraining strategies, and LLM evaluation metrics
Responsibilities
- Design and implement AI automation pipelines using Python (FastAPI, LangChain, spaCy) or Node.js (LLM API wrappers, task automation frameworks)
- Build and optimize OCR-based data extraction systems for invoices, receipts, and other unstructured documents, integrating semantic search and rule-based classification workflows
- Develop custom AI agents for reconciliation, tagging, and anomaly detection across financial and operational datasets
- Fine-tune and orchestrate large language models (LLMs) for document understanding, summarization, and context-aware entity or workflow matching
- Deploy scalable inference pipelines on GCP Vertex AI, AWS Lambda, or Dockerized microservices, ensuring reliability and cost efficiency
- Integrate AI results into user-facing workflows via interactive dashboards, APIs, or automation tools, enabling seamless user adoption
- Monitor and maintain model performance through automated evaluation frameworks, triggering retraining pipelines based on data drift or performance degradation
- Collaborate with cross-functional teams (data, DevOps, and product) to ensure AI automation solutions align with business goals and compliance standards
Skills
Python
FastAPI
LangChain
spaCy
Node.js
OCR
NLP
LLMs
GCP Vertex AI
AWS Lambda
Docker
microservices
semantic search
ShyftLabs
Data-driven decision-making solutions for organizations
About ShyftLabs
ShyftLabs helps organizations adopt a data-first approach to their decision-making processes. Their services focus on establishing systems that enable companies to make quicker and more informed decisions based on data analysis. This approach allows businesses to gain insights that can keep them ahead of their competitors. Unlike other companies that may offer generic consulting services, ShyftLabs emphasizes the importance of data in driving decisions, ensuring that organizations can leverage their data effectively to enhance their strategic planning and operational efficiency.
None, CanadaHeadquarters
2018Year Founded
VENTURE_UNKNOWNCompany Stage
Data & Analytics, ConsultingIndustries
11-50Employees
Benefits
Health Insurance
Hybrid Work Options
Professional Development Budget
Risks
Increased competition from startups offering innovative, cost-effective solutions.
Growing demand for in-house analytics teams reducing reliance on consultants.
Rapid AI advancements may outpace ShyftLabs' current technology offerings.
Differentiation
ShyftLabs specializes in data governance, warehousing, and predictive analysis services.
The firm empowers organizations with a data-first approach for decision-making.
ShyftLabs establishes processes for faster, insightful decisions to outpace competition.
Upsides
Increased demand for data governance due to stricter privacy regulations.
Growing interest in predictive analytics in retail for inventory optimization.
Rising adoption of cloud-based BI tools among SMEs for cost-effectiveness.
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