AI Engineer
Mississauga, Ontario, Canada
Contract
4 weeks ago
$100,000 - $120,000 USD
Key skills
PythonAIMachine LearningMLNLPNatural Language ProcessingGenerative AIGenAILLMLarge Language ModelsOpenAIClaudeAnthropicGeminiLlamaRAGLangChainLlamaIndexAgenticPyTorchTensorFlowscikit-learnPandasNumPyHugging FaceMLOpsPineconeData EngineeringFastAPIAzureKubernetesJenkinsGitLab CIArgoCDOpenShiftAzure DevOpsVertex AINeo4jGitLabCI/CDCollaboration
About this role
Role: AI Engineer
Location: Mississauga, ON- Canada
Position Type: Fulltime
Job Description:
- 8-10 years of relevant experience in Apps Development or systems analysis role
- Core AI/ML Foundations:
- 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).
- Generative AI & LLM Expertise:
- 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.
- Programming & Data Engineering:
- 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.
- Deployment & MLOps:
- 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.
- Cloud & Containerization:
- Proven experience with container orchestration platforms like OpenShift or Kubernetes for deploying, managing, and scaling containerized applications in a cloud-native environment.
- Soft Skills:
- Strong problem-solving abilities, excellent collaboration skills for working effectively with cross-functional teams, and the capability to work independently on complex, ambiguous problems.