KX

Time-aware data platform powering AI insights

Generative AI Solutions Developer

AI Application Engineering +1Confirmed live in the last 24 hours

Full-timeOntario, CanadaNo salary listed

About the Job

Requirements

Have 10–15 years of experience building cloud-native data or machine-learning platforms on AWS, Azure, and/or GCP.
Have deep expertise in Python and one of Go, Java, or C++.
Have hands-on experience with embedding models, tokenizer optimization, prompt orchestration, and OpenAI or Anthropic APIs.
Have previously delivered enterprise generative-AI systems such as risk analytics, chatbots, or document generation.
Have a solid grounding in distributed systems, including Kubernetes, service mesh, Kafka, Redis, and GPU scheduling.
Have programming skills in Python, JavaScript, and TypeScript; Python is required, while JavaScript and TypeScript are for web and full-stack AI applications.
Be able to write clean, testable code.
Know linear algebra, including vectors, matrices, and embeddings; probability and statistics; and machine-learning concepts including overfitting, loss functions, and training versus inference.
Have data cleaning and preprocessing skills and knowledge of Pandas, NumPy, and SQL basics.

Responsibilities

Own end-to-end technical design for large language model and generative AI solutions across NVIDIA, AWS, Azure, and GCP stacks.
Translate business use cases into secure, scalable architectures, lead reference implementations, and mentor delivery teams for enterprise deployments, especially in financial-services environments.
Shape multi-cloud architectures for training, fine-tuning, and serving large language models using technologies such as NeMo, Bedrock, Azure OpenAI, and Vertex AI.
Define MLOps and GitOps patterns for the model lifecycle, vector database indexing, retrieval-augmented generation, and guardrailing.
Benchmark and cost-optimize GPU, Grace Hopper, and CPU clusters, including total cost of ownership and carbon impact.
Build production pipelines using Python, FastAPI, LangChain, Ray, Triton, and Airflow.
Establish security and compliance controls, including encryption, identity and access management, SOC 2, FINRA, and GDPR controls.
Support pre-sales and proofs of concept with capital-markets clients.

Desired Qualifications

Have experience with kdb+/q, ClickHouse, or similar time-series stores.
Have contributed to open-source AI frameworks or NVIDIA NGC containers.
Have working knowledge of site reliability engineering and FinOps best practices.

About KX

KX software powers the time-aware data-driven decisions that enable fast-moving companies to outpace competitors, realizing the full potential of their AI investments. The KX platform delivers transformational value by addressing data challenges related to completeness, timeliness and efficiency, ensuring companies understand change over time and can achieve faster, more accurate insights at any scale, cost-effectively.

KX is essential to the operations of the world's top investment banks, aerospace and defence, high-tech manufacturing, healthcare and life sciences, automotive and fleet telematics organizations. The company has established offices and a robust customer base across North America, Europe, and Asia Pacific.

Overview Of The Role

Own the end-to-end technical design for large-language-model (LLM) and generative-AI solutions across NVIDIA, AWS, Azure and GCP stacks. You will translate business use-cases into secure, scalable architectures, lead reference implementations, and mentor delivery teams for enterprise deployments—especially in financial-services environments.

Key Responsibilities

Shape multi-cloud architectures for training, fine-tuning and serving LLMs (e.g., NeMo, Bedrock, Azure OpenAI, Vertex AI).
Define MLOps/GitOps patterns for model lifecycle, vector–DB indexing, retrieval-augmented generation (RAG) and guard-railing. Benchmark and cost-optimize GPU, Grace Hopper and CPU clusters (TCO & carbon impact).Build production pipelines in Python (FastAPI, LangChain, Ray, Triton, Airflow).
Establish security and compliance controls (encryption, IAM, SOC 2, FINRA,GDPR).
Support pre-sales and proofs-of-concept with capital-markets clients.

Skills

Programming

Python (must-have)
JavaScript / TypeScript (for web + full-stack AI apps)
Writing clean, testable code

Math & ML Basics

Linear algebra (vectors, matrices, embeddings)
Probability & statistics
ML concepts: overfitting, loss functions, training vs inference

Data Skills

Data cleaning & preprocessing
Pandas, NumPy
SQL basics

Essential Experience

10-15 yrs building cloud-native data or ML platforms on AWS, Azure and/or GCP. Deep expertise in Python plus one of Go, Java
or C++.Hands-on with embedding models, tokenizer optimisation, prompt orchestration and OpenAI/Anthropic APIs. Prior delivery of enterprise Gen-AI systems (risk analytics, chatbots, document generation etc.).Solid grounding in distributed systems (K8s, service mesh, Kafka, Redis, GPU scheduling).

Preferred Qualifications

Experience with kdb+/q, ClickHouse or similar time-series stores. Contributions to open-source AI frameworks or NVIDIA NGC containers. Working knowledge of SRE/FinOps best practice.

Location & Workplace Type

This position takes on a Hybrid working model based in Ontario, Canada.

Why Choose KX

Data Driven: We lead with instinct and follow fact.

Naturally Curious: We lean in, listen and learn fast.

All In: We take ownership, take on challenges and give it our all.

Benefits

Competitive Salary
Individually tailored training and skills development
Private healthcare package and Employee Assistance Programme
Enhanced maternity and paternity package
Wellness Days and Volunteer Days

About the Company

KX

Time-aware data platform powering AI insights

KX provides a data and analytics foundation that delivers time-aware, complete, and timely data to power AI-enabled decision making. It collects and harmonizes large datasets, tracks how data changes over time, and runs scalable analytics and dashboards optimized for AI workloads. This helps fast-moving organizations generate faster and more accurate insights at any scale with cost efficiency. Its focus on change-over-time data and scalable performance helps sectors like finance, aerospace, manufacturing, healthcare, automotive, and fleet telematics maximize AI value and outpace competitors.

Company size: 201-500
Company stage: Not listed
Total funding: Not listed
Headquarters: Newry, United Kingdom
Founded: 1993

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