Strong hands-on expertise in Python and end-to-end AI/ML model development, including data preparation, feature engineering, training, evaluation, and production integration.
Compensation
Pay range: $70-75/hr C2C
Responsibilities
Translate business requirements into scalable AI/ML solutions
Collaborate with architects, data engineers, application teams, QA, and business stakeholders
Strong hands‑on expertise in Python and end‑to‑end AI/ML model development, including data preparation, feature engineering, training, evaluation, optimization, and production integration.
Compensation
Pay Range: $55-60/Hour on W2/C2C (All Inclusive)
Responsibilities
Analyze business and technical requirements and design scalable AI/ML solutions aligned with enterprise architecture, security, privacy, and responsible AI standards.
Develop, train, evaluate, tune, and document machine learning, deep learning, NLP, and Generative AI models using Python, TensorFlow, and/or PyTorch.
Design and implement LLM‑based applications and Retrieval‑Augmented Generation pipelines using embeddings, vector databases, prompt engineering, orchestration frameworks, and appropriate evaluation methods.
Collaborate with data engineering, application, cloud, DevOps, QA, architecture, product, and business teams to integrate models into enterprise platforms through APIs and microservices.
Implement production‑readiness practices including automated testing, CI/CD, containerization, model versioning, monitoring, observability, drift detection, performance optimization, troubleshooting, and continuous improvement.
Lead the product strategy, roadmap, and business case for Hitachi iQ, an AI infrastructure and AI Factory platform, while managing a team of product managers and collaborating cross‑functionally across engineering, marketing, sales, and partner organizations.
Compensation
As required by the equal pay and transparency acts, the expected base salary for this position is: $175K to $250K. Expected on-target earnings: $205K to $295K
Responsibilities
Own the product strategy, roadmap, and business case for Hitachi iQ
Drive NVIDIA and broader ecosystem certification strategy
Build and defend competitive positioning against AI infrastructure, storage, and data‑platform competitors
Define and prioritize AI use cases that the platform must support
Act as executive spokesperson for the portfolio in partner councils, technical advisory forums, customer briefings, and internal strategy reviews
Requirements
Bachelor's degree in a technical field (Computer Science, Engineering) or equivalent experience
Analyze business and technical requirements and design scalable AI/ML solutions
Develop, train, evaluate, tune, and document machine learning, deep learning, NLP, and Generative AI models using Python, TensorFlow, and/or PyTorch
Design and implement LLM-based applications and Retrieval-Augmented Generation pipelines using embeddings, vector databases, prompt engineering, orchestration frameworks, and appropriate evaluation methods
Collaborate with data engineering, application, cloud, DevOps, QA, architecture, product, and business teams to integrate models into enterprise platforms through APIs and microservices
Implement production-readiness practices including automated testing, CI/CD, containerization, model versioning, monitoring, observability, drift detection, performance optimization, troubleshooting, and continuous improvement
The Lead Engineer, Evidence Management is responsible for engineering leadership, application architecture, and delivery governance for the Evidence Management digital ecosystem spanning Scientific Affairs and Regulatory Affairs. This is primarily an Appian-first, AI-enabled, and ecosystem-oriented engineering role.
Locations (2)
Limerick, Munster, Ireland
Raritan, New Jersey, United States
Compensation
The anticipated base pay range for this position is: €71,200.00 - €123,165.00. In addition to base pay, we offer the following benefits*: an annual bonus with set target (% of pay) depending on pay grade / location, where the actual amount is based on the employees’ and companies’ performance of the previous calendar year, or sales commissions.
Responsibilities
Own and maintain the application architecture strategy and engineering roadmap for evidence management capabilities across Regulatory Affairs and Scientific Affairs
Design scalable, interoperable, and compliant solutions across regulatory platforms, Appian applications, data flows, workflows, integrations, content repositories, and analytics capabilities
Translate complex regulatory and scientific evidence processes into practical application designs, engineering patterns, data models, and delivery backlogs
Drive design governance for new capabilities, major enhancements, technical debt remediation, platform modernization, and cross-system interoperability
Ensure solution designs align with enterprise standards for cloud, cybersecurity, privacy, lifecycle management, supportability
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