Software Developer (ID#4829)

New Value Solutions, a national IT consulting company, is seeking an AI Engineer to work on an innovative project harnessing AI and data analytics.

Responsibilities:

1. Assigned to high complex work.
2. Fully participates in activities supporting the team’s delivery and continuous improvement.
3. Assists other roles with their work.
4. Mentors team members.
5. Promotes a respectful and collaborative culture.

Mandatory Requirements:

1. Undergraduate degree in computer science or STEM (Science, Technology, Engineering, Math) and 6+ years of equivalent work experience in IT.
2. Graph Theory & Knowledge Graphs: Experience building and querying knowledge graphs to represent structured knowledge and relationships, particularly in the context of enhancing retrieval-augmented generation (RAG) models. Familiarity with graph databases such as Neo4j, ArangoDB, or Amazon Neptune is valuable for structuring and storing graph data.
3. Retrieval-Augmented Generation (RAG): Expertise in RAG models, which combine retrieval-based and generative approaches to improve AI's ability to generate contextually relevant and accurate responses by retrieving data from external sources like documents, databases, or web data. Experience implementing or fine-tuning models such as RAG from Facebook AI, which integrates retrieval-based techniques (e.g., Dense Retriever, BM25) with generative models (e.g., GPT, T5). Exposure to / experience with Advanced RAG techniques such as Lazy GraphRAG, DRAG (Dynamic RAG), MultiHop RAG for increasing accuracy and reducing costs.
4. Information Retrieval and Search Algorithms: Deep knowledge of information retrieval (IR) techniques for implementing document or knowledge retrieval systems that form the basis of RAG. Experience with embedding-based search, nearest neighbour search, and indexing techniques using libraries like FAISS, Annoy, or Elasticsearch to retrieve relevant information from large datasets.
5. Graph Data Processing & Manipulation: Ability to preprocess and manipulate graph data for tasks like entity resolution, graph pruning, and graph-to-text generation. Knowledge of graph-based algorithms such as PageRank, centrality measures, and community detection to enhance the accuracy of generative AI models based on graph data.
6. Large-Scale Model Training & Optimization: Experience with distributed training techniques for large generative models, especially when using graph data that may require massive computational resources. Familiarity with optimization methods, including gradient-based optimization and multi-task learning, to fine-tune AI models that involve both graph learning and language generation. Experience with techniques for optimizing model performance for deployment, including hardware acceleration (e.g., GPUs/TPUs), pruning, and quantization.
7. Embedding Models & Vector Representations: Expertise in building and fine-tuning graph embeddings and sentence embeddings that capture semantic relationships within graph-based structures, improving the quality of downstream generative AI tasks. Proficiency with vector databases and embedding management tools like CosmosDB or Pinecone for efficiently querying and using vector-based representations of graph data.
8. Model Evaluation & Metrics: Strong understanding of performance metrics for generative AI models, including BLEU, ROUGE, perplexity, and generation quality. Ability to evaluate retrieval-augmented models through both precision/recall of retrieved information and relevance of generated output, ensuring that the model produces contextually relevant results when using graph data. Diversity Metrics: In generation tasks, metrics such as distinctness or novelty measure how diverse the generated outputs are, helping assess diversity and creativity of generation.
9. Cloud & Deployment Technologies: Familiarity with cloud platforms (Azure) for deploying graph-based AI models in production environments, ensuring scalability and low-latency retrieval. Knowledge of deployment strategies such as containerization (AKS), microservices, and serverless architectures for deploying generative AI applications that leverage graph data for real-time use cases.
10. Other requirements: Proficiency in programming languages like Python, which is widely used for AI and machine learning, or other languages commonly used in AI. Knowledge of transfer learning techniques to leverage existing models and datasets for improved task performance, even with limited labeled data. ETL (Extract, Transform, Load): Knowledge of ETL processes for data wrangling, preparing data for graph databases, and integrating various data sources.

If you have this expertise and are able to work in Canada, please submit your resume. While we thank all candidates in advance for their application, only those candidates who are shortlisted will be contacted.

ID# 4829

The hourly rate range for this position is $75 - $95, with the final rate based on consultant experience and fit for the role.