Environment Modeller Job at Weta Digital (Vancouver)

Avp ml and genai model validation

Embark on a transformative journey as an AVP ML and GenAI Model Validation at Ba...

Location

India , Noida

Salary:

Not provided

Barclays

Expiration Date

Until further notice

Requirements

Highly numerate, evidenced by an Honours degree, master’s, or PhD (or equivalent) in a quantitative discipline such as Computer Science, Mathematics, Physics, Operational Research, Economics, Engineering, or Finance
Experience developing, validating, or independently reviewing best‑in‑class tools and AI/ML models for large financial institutions or comparable regulated environments, with the ability to effectively challenge and improve model methodologies and model management processes
Strong analytical and technical expertise in AI/ML modelling, including experience with ML algorithms (e.g., XGBoost, LightGBM, Random Forest), NLP models, and deep learning frameworks (e.g., TensorFlow, PyTorch, Keras)
Experience developing or validating AI/ML models on large datasets in Hadoop/AWS environments
Designed, fine‑tuned, and shipped Generative AI models - transformer‑based LLMs, RAG systems for grounded retrieval, and GANs-using PyTorch/TensorFlow and Hugging Face, including prompt engineering, RLHF, and rigorous evaluation of GenAI models
Hands‑on experience with Generative AI technologies, including Large Language Models, Transformers, retrieval‑augmented generation (RAG), and prompt‑based systems
familiarity with LangChain and LangGraph
Experience designing, developing, validating, or reviewing agent‑based AI architectures, including multi‑agent orchestration, task planning, memory management, and safeguard mechanisms
Sound understanding of financial services regulatory expectations and model risk guidance (e.g., SR 11‑7, SS1/23, IFRS 9, EU AI Act etc.)
Strong communication and influencing skills, with the ability to produce high‑quality written material for both technical and senior non‑technical audiences

Job Responsibility

Validation of models for their intended use and scope, commensurate with the complexity and materiality of the models
Approval or rejection of a model or usage based on assessment of the model’s conceptual soundness, performance under intended use and the clarity of the documentation of the model’s inherent risks, limitations and weaknesses
Assessment of any compensating controls used to mitigate Model risk
Documentation of validation findings and recommendations in clear and concise reports, providing actionable insights for model improvement
Evaluation of the coherence of model interactions and quality of Large Model Framework aggregate results that generate output for regulatory submissions or management decision making and planning
Design of the framework and methodology to measure and, where possible, quantify model risk, including the assessment of framework level uncertainty

What we offer

Hybrid working
Modern workspaces, collaborative areas, and state-of-the-art meeting rooms
Facilities include wellness rooms, on-site cafeterias, fitness centers, and tech-equipped workstations

Fulltime

Technical Program Manager - Pre-silicon Team

We are seeking a Technical Program Manager to coordinate the development and qua...

Location

United States , Austin

Salary:

171200.00 - 256800.00 USD / Year

AMD

Expiration Date

Until further notice

Requirements

Use of AI tools for planning, development and debug
Familiarity with pre-silicon methodologies, software modeling, emulation, and hybrid modeling environments
Program Management experience with software development and release lifecycle
Skilled in collaboration tools: Confluence, JIRA and MS Office suite (Excel, PowerPoint, Project)
Knowledge of x86 platform architecture and debug
Excellent communication, management, and presentation skills
Adept at collaboration among top-thinkers and senior architects with strong interpersonal skills to work across teams in different geographies
Bachelor’s or Master’s degree in related discipline preferred

Job Responsibility

Define, plan, and drive SimNow development and model qualification involving multi-component environments (AMD IPs, SoCs, HW, SW, FW)
Comprehend the SOC as a complete system which includes HW (Silicon), FW, BIOS & SW and ensure that SimNow models are aligned to enable pre-silicon feature enablement plans
Develop and improve processes for project tracking and risk mitigation
Track and report status vs. objectives throughout the lifecycle
Liaison between teams to implement latest methodology and shift-left initiatives
Communicate progress and conduct regular reviews with AMD senior management and customers
Raise red flags proactively when risks emerge and partner with technical teams to resolve issues
Drive resource planning for development, qualification, and support
Manage dependencies with design teams for SimNow model development
Craft, lead, and continually improve program-level processes and methodologies

Fulltime

Principal Engineer

The Principal AI/ML Operations Engineer leads the architecture, automation, and ...

Location

United States , Pleasanton, California

Salary:

251000.00 - 314500.00 USD / Year

BlackLine

Expiration Date

Until further notice

Requirements

Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field
10+ years in ML infrastructure, DevOps, and software system architecture
4+ years in leading MLOps or AI Ops platforms
Strong programming skills in languages such as Python, Java, or Scala
Expertise in ML frameworks (TensorFlow, PyTorch, scikit-learn) and orchestration tools (Airflow, Kubeflow, Vertex AI, MLflow)
Proven experience operating production pipelines for ML and LLM-based systems across cloud ecosystems (GCP, AWS, Azure)
Deep familiarity with LangChain, LangGraph, ADK or similar agentic system runtime management
Strong competencies in CI/CD, IaC, and DevSecOps pipelines integrating testing, compliance, and deployment automation
Hands-on with observability stacks (Prometheus, Grafana, Newrelic) for model and agent performance tracking
Understanding of governance frameworks for Responsible AI, auditability, and cost metering across training and inference workloads

Job Responsibility

Define enterprise-level standards and reference architectures for ML-Ops and AIOps systems
Partner with data science, security, and product teams to set evaluation and governance standards (Guardrails, Bias, Drift, Latency SLAs)
Mentor senior engineers and drive design reviews for ML pipelines, model registries, and agentic runtime environments
Lead incident response and reliability strategies for ML/AI systems
Lead the deployment of AI models and systems in various environments
Collaborate with development teams to integrate AI solutions into existing workflows and applications
Ensure seamless integration with different platforms and technologies
Define and manage MCP Registry for agentic component onboarding, lifecycle versioning, and dependency governance
Build CI/CD pipelines automating LLM agent deployment, policy validation, and prompt evaluation of workflows
Develop and operationalize experimentation frameworks for agent evaluations, scenario regression, and performance analytics

What we offer

short-term and long-term incentive programs
robust offering of benefit and wellness plans

Fulltime

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