
AI Engineer
Role summary
We are seeking an experienced AI Engineer with 8-10 years of relevant experience in application development or systems analysis. The role requires strong foundational knowledge in GenAI, ML, Data Science, Statistics, NLP, Neural Networks, and LLMs. Key responsibilities include extensive hands-on experience with leading LLMs (Gemini, OpenAI, Claude, Mistral, Llama), deep expertise in RAG pipelines, building and deploying LLM-based applications using platforms like Vertex AI and Hugging Face, and developing prompt engineering strategies. Proficiency in Python with libraries like Pandas, NumPy, PyTorch, TensorFlow, LangChain, and LlamaIndex is essential. The role also demands hands-on experience with MLOps, CI/CD principles, containerization (Kubernetes/OpenShift), and vector databases. Strong problem-solving and collaboration skills are required.
POSITION DETAILS:
Role: AI Engineer
Location: Mississauga, ON- Canada
Position Type: Fulltime
Salary- CAD 120K
Note- We have 10+ Open roles.
Skills Required:
GenAI, Python, LLM (Google Gemini, OpenAI models, Anthropic Claude, Mistral, Llama), MLOps, Retrieval-Augmented Generation (RAG)
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.
Thanks & Regards,
Yuvi
Senior Technical Recruiter
Email: yuvi@themesoft.com
Web: www.themesoft.com
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