Artificial Intelligence Engineer

Tekshapers; Mississauga; On-site; CAD 100,000 - 120,000; Full time

Job summary

A tech solutions provider is seeking an experienced professional for an Apps Development role focusing on GenAI technologies. The ideal candidate has 8-10 years of experience and strong expertise in LLMs, machine learning, and data science. You'll be responsible for deploying LLM-based applications and developing robust solutions using various tools and frameworks in a cloud-native environment. Excellent problem-solving and collaboration skills are essential, alongside hands-on experience in Python programming.

Qualifications

• 8-10 years of relevant experience in Apps Development or systems analysis role.
• Experience with leading LLMs such as Google Gemini, OpenAI models, etc.
• Proven ability to build, tune, and deploy LLM-based applications.

Responsibilities

• Deploying GenAI-based models to production environments.
• Building robust prompt engineering strategies and reusable prompt templates.
• Integrating generative AI with enterprise applications using APIs.

Skills

Python programming proficiency

Strong foundational knowledge in GenAI

Machine Learning modeling

Data Science

Statistics

NLP fundamentals

Neural Networks

LLMs experience

Experience with RAG pipelines

Prompt engineering strategies

MLOps principles

CI/CD tools expertise

Container orchestration

Problem-solving abilities

Collaboration skills

Tools

TensorFlow

PyTorch

Transformers

FastAPI

LangChain

LlamaIndex

OpenShift

Kubernetes

Jenkins

GitLab CI

Azure DevOps

ArgoCD

Pandas

NumPy

scikit-learn

Job description

• 8-10 years of relevant experience in Apps Development or systems analysis role
• Strong foundational knowledge in GenAI , Machine Learning (ML modeling), Data Science, Statistics, and AI fundamentals, including Natural Language Processing (NLP), Neural Networks, and Large Language Models (LLMs).
• Extensive hands‑on experience with leading LLMs such as Google Gemini, OpenAI models, Anthropic Claude, Mistral, Llama, and various other open‑source LLMs.
• Critical: Deep working knowledge and hands‑on experience with Retrieval‑Augmented Generation (RAG) pipelines, including advanced RAG techniques and their detailed implementation.
• Proven ability to build, tune, and deploy LLM‑based applications using platforms like Vertex AI, Hugging Face, etc.
• Expertise in developing robust prompt engineering strategies, prompt tuning, and creating reusable prompt templates.
• Hands‑on experience with agentic framework‑based use case implementation.
• Working knowledge of Guardrails and methodologies for assessing the performance and safety of GenAI features.
• Strong programming proficiency in Python is a must, including extensive experience with libraries such as Pandas, NumPy, scikit‑learn, PyTorch, TensorFlow, Transformers, FastAPI, Seaborn, LangChain, and LlamaIndex.
• Proficiency in integrating generative AI with enterprise applications using APIs, knowledge graphs, and orchestration tools.
• Hands‑on experience with various vector databases (e.g., PG Vector, Pinecone, Mongo Atlas, Neo4j) for efficient data storage and retrieval.
• Experience in dealing with large amounts of unstructured data and designing solutions for high‑throughput processing.
• Critical: Hands‑on experience deploying GenAI‑based models to production environments.
• Strong understanding and practical experience with MLOps principles, model evaluation, and establishing robust deployment pipelines.
• Strong expertise in CI/CD principles and tools (e.g., Jenkins, GitLab CI, Azure DevOps, ArgoCD) for automated builds, testing, and deployments.
• Proven experience with container orchestration platforms like OpenShift or Kubernetes for deploying, managing, and scaling containerized applications in a cloud‑native environment.
• Strong problem‑solving abilities, excellent collaboration skills for working effectively with cross‑functional teams, and the capability to work independently on complex, ambiguous problems.