Latest AI/Data/Cloud Talent

We would like to share some of the people we’re working with at the moment. Please see some brief overviews below of the talent available now.

Lead AI Engineer

A Machine Learning Specialist with a focus in constructing intricate neural networks and an emphasis on scalable Generative AI.

As a Lead AI Engineer, the candidate designed and developed a multi-modal, context-aware generative language and vision model (VLM/VLLM) to address intra-class classification challenges, significantly enhancing security and processing speed. They also designed and implemented an ensemble deep learning algorithm (Gen AI) for image classification and feature extraction, improving classification accuracy and reducing processing time. The candidate is Auckland based and comfortable with office based, hybrid or remote work options.

Technical Stack & Tools:

Languages: Python, C++, RUST, SQL, Bash
ML Frameworks: PyTorch, TensorFlow, JAX, HuggingFace Transformers
Cloud Services: AWS (SageMaker, Lambda, EC2, S3, Glue, ECR, DynamoDB), Azure (Data Factory, Synapse Analytics, Databricks, Event Hub)
Quantization: QPTQ, NF4, GGUF, OpenVINO, TensorRT
Other: Kubernetes, Docker, Argo Workflows, BentoML, Flask, FastAPI, Git, JIRA

Ideal Salary Expectation: $160k-$170k

Computer Vision Engineer

The candidate is a Data Scientist/Computer Vision Engineer with 3+ years of industry experience delivering AI & Machine Learning solutions to customers, and 1 years of research experience with two papers published in the machine learning and computer vision field. He emphasises working collaboratively within Agile environments to ensure best practices and seamless integration of Computer Vision solutions with upstream and downstream systems within a Smart City Intelligent Operations framework.

He has experience implementing binary classification models and regression models to solve business cases and AML prototype solutions which include anomaly detection models. He has worked with text vector space models to classify the articles.

Technical Stack & Skills:

● Programming Languages: Python, R, Java, SQL

● Agile Software: JADE

● Big Data: PySpark, Airflow

● Cloud Platforms: AWS, Azure

● MLOps: Data, model, infrastructure and monitoring, Google ML Production Rubric

● Machine Learning: Classification & Regression Modelling, Time Series Forecasting, Anomaly Detection

● Computer Vision: General CV

● NLP: Nature Language Processing

● Visualization: PowerBI & Dash

Ideal Salary Expectation: $115k-$120k

Intermediate Data Scientist

The candidate is a Data Scientist with experience in fast growth startups. He is skilled in leveraging Python, R, MySQL, Power BI, and BigQuery to develop insightful dashboards and predictive models. He is experienced in data migration to graph databases and developing graph algorithms.

His work has involved;

● QA Tool Development: Designed and developed an application for Quality Assurance teams to streamline the collection of training data.

● Classification Model Development: Built a robust text classification model using collected training data to support accurate vehicle and part variant disambiguation. Has used BERT-based NLP techniques for this domain-specific classification.

● Automated Vertex AI Pipelines: Developed and implemented Vertex AI Pipelines on GCP to automate the entire ML workflow, including data processing, splitting,

tokenizing, model training, evaluation, and deployment as a Vertex AI endpoint for real-time inference.

● Production Deployment: Successfully deployed the model by creating API endpoints for integration into production systems.

Technical Stack & Skills:

● Programming Languages: Python, R

● Databases: MySQL, BigQuery, Neo4j Graph Database (Cypher Query Language)

● Cloud Platforms: Google Cloud Platform (GCP), Vertex AI Pipelines

● Visualization: Power BI, SAP Analytics Cloud

● Other: Pandas, NumPy, Matplotlib, BERT-based NLP techniques

Ideal Salary Expectation: $75k-$85k

Junior Data Engineer

This junior engineer is a highly motivated and results-oriented data scientist with a strong background in statistical analysis, machine learning, and data visualization. With experience across various industries, including manufacturing, telecommunications, and finance, demonstrating adaptability and ability to apply data-driven solutions to diverse business challenges.

Her expertise lies in developing and deploying machine learning models, conducting statistical analyses, and creating impactful data visualizations to communicate findings effectively. She is skilled in working with large datasets, cleaning and preparing data for analysis, and using a variety of tools and techniques to extract meaningful insights.

Previous work has involved;

● Predictive Maintenance for Manufacturing: Developed a machine learning model to predict machine failures in a manufacturing setting, resulting in a significant reduction

in downtime and maintenance costs. This project involved data collection from various sensors, feature engineering, model selection, and deployment of the model into a production environment.

● Customer Churn Prediction for Telecommunications: Built a customer churn prediction model for a telecommunications company, identifying key factors contributing to churn and enabling targeted retention strategies. This project involved

analyzing customer demographics, usage patterns, and interaction history to identify at-risk customers.

● Fraud Detection for Financial Services: Developed a fraud detection model for a financial institution, identifying fraudulent transactions and minimizing financial losses. This project involved analyzing transaction data, identifying suspicious patterns, and implementing a real-time fraud detection system.

● Inventory Optimisation: Led the implementation of an inventory management system, resulting in a 15% reduction in carrying costs while ensuring optimal stock levels to

meet demand.

Key Skills

● Programming Languages: Python, R, SQL

● Machine Learning: Regression, Classification, Clustering, Time Series Analysis

● Statistical Analysis: Hypothesis Testing, ANOVA, Regression Analysis

● Data Visualization: Tableau, Power BI, Matplotlib, Seaborn

● Cloud Computing: AWS (Amazon Web Services)

● Data Wrangling: Data Cleaning, Data Transformation, Feature Engineering

● Databases: SQL databases (PostgreSQL, MySQL), NoSQL databases (MongoDB)

● Other: Model Deployment, A/B Testing

Ideal Salary Expectation: $100k-$105k

Data Engineer

Candidate is a highly experienced full-stack Data and AI Specialist with a strong track record of achievements in data analytics, machine learning, and data engineering. Based in Christchurch, he possesses expertise in building Business Intelligence, Data Warehouse, and Data Lake systems from the ground up, including gathering requirements, consolidating data, designing analysis models, and deploying dashboards. He is also adept at developing and deploying predictive analytics and machine learning models.

His recent experience includes roles at a large insurance company as a Data Engineer where he’s built a Data Lakehouse and Data Pipelines on AWS.

Technical Stack & Skills:

● Programming Languages: Python, SQL, C#, COBOL, Bash, PowerShell, Java, Scala, VBA

● Databases: SQL Server, Redshift, BigQuery, Snowflake, PostgreSQL, MySQL, Oracle, Cosmos DB, Firebird, GreenPlum, MongoDB

● Platforms and Services: AWS, Azure, GCP, Data Lakehouses (Iceberg, Delta, Hudi), Databricks, Fabric, SSIS, SSAS, SSRS, SharePoint, Dynamics 365

● Frameworks and Tools: Spark, dbt, Airflow, Git, Docker, Terraform, Pentaho, Jenkins, Qlik Replicate, Qlik Compose, Power Automate, Airbyte, Fivetran, Talend, Nifi, Beam

● Data Analytics: Power BI, Qlik Sense, Tableau, SAS, Domo, Looker, Metabase, Superset

● Data Modeling: Kimball Dimensional and Data Vault

● Machine Learning &; AI: Generative AI, LLM (GPT, Claude, Llama), RAG, LangChain, Agents, Ensembles, Scikit-Learn, Regressions, Support Vector Machines, Bayesian,

Time Series, KNN, PCA, K-Means, Recommendation Systems, Numpy, Pandas, Dask

Ideal Salary Expectation: $130k-$140k

AI/ML Consultant

Senior Applied AI/ML consultant with a strong emphasis on MLOps and ground-up dataagility, he helps enterprises leverage their data while maintaining cost-effectiveness and

scalability. He has a proven track record of working on cutting-edge AI/ML problems across various sectors globally and within New Zealand. His expertise lies in building AWS solutions for data wrangling, visualization, statistical analysis, machine learning, data pipelines, and automation.

His recent experience includes working as the sole AI/ML Specialist at a cyber consulting firm within the Big Data practice, where he advocated for data agility and complete data operation automation using AWS services. He is available for remote based contract work, short or long term assignments.

Technical Stack & Skills:

● Cloud Platforms: AWS (SageMaker, Glue, EMR, Fargate, Kinesis, Athena, RedShift)

● Data Technologies: Spark, Hadoop, MapReduce, Kafka, Airflow, Apache Tools

● ML Frameworks: TensorFlow, PyTorch, JAX, tinygrad

● Programming Languages: Python, ASM, Julia, Rust, SQL

Hourly Rate Negotiable depending on assignment scope and length

Senior Data & Machine Learning Engineer

The candidate is a Senior Data Platform Engineer and a certified Machine Learning Engineer with over 10 years of experience in developing end-to-end innovative applications. Based in Christchurch, she brings a strong technical background in cloud technologies,

platform engineering, data engineering, and machine learning.

In a recent project she led the design and implementation of an MLOps platform on AWS, streamlining the machine learning model lifecycle from development to production. This involved using Terraform, CloudFormation, and Docker for infrastructure management and deployment. She leveraged AWS services like SageMaker, Lambda, CloudWatch, etc., to implement end-to-end automated pipelines for data processing, training and deploying

models.

Another project involved building a personalised recommendation system for a large

telecommunications company. She built the infrastructure, data pipelines, and reporting dashboards to enable real-time tracking and analysis of model performance using AWS services such as Athena, SageMaker, and Lambda, with Terraform used to automate the infrastructure setup. She delivered a few prioritization models and a simulation environment, which enabled testing and validation of different models & performance in a controlled setting.

A prioritization API service was also delivered to deploy prioritisation model and provide prediction service.

Technical Stack & Skills:

● Cloud Platforms: AWS, GCP, Azure

● Programming Languages: Python, SQL

● Tools & Technologies: Terraform, SageMaker, Lambda, Docker, TensorFlow, Pandas, scikit-learn, CloudFormation, Gitlab CI, Athena, Step Functions, CloudWatch, S3, DynamoDB, Glue, Kinesis, DBT, FastAPI, MS SQL Server, JADE, Shiny, Hadoop, Splunk, Elasticsearch, Kibana

● Certifications: AWS Certified Machine Learning - Specialty, GCP Professional Cloud Architect, GCP Professional Data Engineer, Azure Data Scientist Associate, Terraform Associate

Ideal Salary Expectation: Circa $250k

Lead Data Scientist

The candidate is a Machine Learning Engineer based in Christchurch, New Zealand, with experience leading technical teams and architecting data solutions across diverse industries. They possess a strong computer engineering background combined with practical experience in data engineering, machine learning, and data science.

Key Experience:

● Technical team lead for the commercial next best action project for client British Telecom.

● Designed and implemented a production-grade reinforcement learning system for optimizing decision-making related to marketing and sales.

● Designed and implemented an online stratification algorithm for scaled-up real-time stratification of users for model A/B testing.

● Served in an advisory role at client Prospa for new MLOps infrastructure and processes, covering topics such as feature store, model hosting, architecture, model governance, and more.

● Developed a fully functioning prototype for a large mining sector client for a geological block model prediction system.

Key Skills:

● Cloud Platforms: AWS, Azure

● Programming: Python, SQL

● ML Techniques: Reinforcement Learning, Anomaly Detection, Predictive Modeling

● Data Engineering: Data Pipelines, Data Platform Architecture

● Other: A/B Testing, DevOps principles, Data Visualization

Ideal Salary Expectation: $180k-$190k