AI Engineer

Role: AI Engineer

Location: Mississauga, ON- Canada

Position Type: Fulltime

Client- Citi Bank

Salary- CAD 120K

I wanted to share an update on the interview process for the AI Engineer role with Citi.

Candidates who clear the internal interview with Altimetrik will be required to take the Karat interview in person at the Altimetrik office.

Please submit only those candidates who are willing to come for the in-person Karat interview.

Let me know if you have any questions.

Kindly check with your candidates and share the list of those who are comfortable attending the in-person interview.

Interview Process (3 Levels):

1.          L1 – Altimetrik Internal Panel (Teams/Zoom)

2.          Karat Interview – In Person at Altimetrik Office

3.          Discussion with Project Team (Teams/Zoom)

Altimetrik Office Location in Canada,

151 Yonge Street 11th Floor Toronto ON - M5C 2W7

Note- We have 10+ Open roles.

Skill

Years of Experience

GenAI

Python

LLM (Google Gemini, OpenAI models, Anthropic Claude, Mistral, Llama)

MLOps

Retrieval-Augmented Generation (RAG)

JD

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.