Machine Learning Engineer designing and implementing AI systems focused on Japanese language challenges at Woven by Toyota. Involves technical R&D, system design, and collaboration with cross - functional teams.
About the role
- AI/ML Engineer building enterprise GenAI capabilities for Safe-Guard Products International. Developing scalable AI solutions across various platforms and improving employee workflows.
Responsibilities
- Design and implement enterprise GenAI platform components, including:
- LLM orchestration (LangChain, LangGraph) and provider abstraction (Azure OpenAI, AWS Bedrock, GCP Vertex).
- Prompt and workflow templates.
- Evaluation frameworks and telemetry.
- Guardrails for safety, compliance, and data protection.
- Define standardized GenAI patterns that product and engineering teams can reuse across the organization.
- Partner with architecture, security, and compliance teams to ensure GenAI solutions align with enterprise policies and Responsible AI principles.
- Establish technical standards and best practices for GenAI development across onshore and offshore teams.
- Support experimentation by business users while ensuring guardrails, monitoring, and auditability.
- Work with business stakeholders to identify high-impact employee workflows where GenAI can improve productivity, decision-making, and quality.
- Partner with IT and business teams to expand and optimize the use of M365 Copilot across employee roles (operations, customer service, leadership, support teams).
- Design and implement integrations that connect enterprise data, knowledge bases, and workflows to M365 Copilot using approved patterns.
- Build ML and GenAI solutions that support multiple business domains, including:
- Claims loss mitigation and fraud detection.
- Customer and agent experience.
- Operational efficiency and internal productivity.
- Combine classical ML, rules engines, and LLMs into hybrid systems where appropriate.
- Ensure solutions provide explainability, traceability, and measurable business value.
- Build enterprise-grade RAG pipelines over structured and unstructured data (policies, procedures, internal documentation, historical records).
- Implement document ingestion, chunking, embeddings, re-ranking, and citation-based outputs.
- Ensure GenAI responses are grounded, accurate, and auditable.
- Define and implement evaluation strategies for ML and GenAI solutions, including:
- Accuracy, precision/recall, hallucination rates.
- Latency and cost metrics.
- User adoption and satisfaction.
- Build monitoring and feedback loops to continuously improve AI systems.
- Apply Responsible AI practices: PII protection, access controls, logging, and audit trails.
Requirements
- 3–4 years of experience in AI/ML engineering, applied data science, or related roles.
- Strong proficiency in Python and SQL.
- Solid foundation in machine learning (classification, anomaly detection, model evaluation).
- Hands-on experience with GenAI systems, including:
- Prompt design and testing.
- Embeddings and vector search.
- Retrieval-Augmented Generation (RAG).
- Structured outputs and validation.
- Experience building and deploying APIs/services (FastAPI or similar).
- Familiarity with containerization (Docker) and CI/CD.
- Experience working in cloud environments (Azure, AWS, and/or GCP).
- Strong communication skills and comfort working in product-facing and enablement roles.
- Experience building internal platforms, shared services, or enablement frameworks.
- Exposure to M365 Copilot extensibility or enterprise productivity tooling.
- Familiarity with ML/LLMOps tools (model registries, experiment tracking, prompt/version management).
- Knowledge of Responsible AI, governance, and enterprise data privacy practices.
- Experience in regulated industries (insurance, auto warranty, financial services).
Benefits
- Medical, Dental, and Vision Insurance
- Flexible Spending Account
- Health Savings Account
- 401(k) Plan with Company Match
- Company-paid Short-Term and Long-Term Disability
- Company-paid Life Insurance
- Paid Holidays and Vacation
- Employee Referral Program
- Employee Assistance Program
- Wellness Programs
- Paid Community Service Opportunities
- Tuition Reimbursement
- Ongoing Training & Personal Development
- And More!
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
Principal Software Engineer leading MLOps within Analytics Platform at Sun Life. Focused on AWS and machine learning operations, collaborating across technical and business teams.
Machine Learning Engineer designing and optimizing deep learning models for safety - critical environments at Destinus. Shaping the future of high - speed, autonomous flight technologies.
Machine Learning Engineer optimizing personalization systems for Spotify's audio streaming service. Collaborating with cross - functional teams to enhance user experience and deliver recommendations.
Principal Machine Learning Engineer developing ML and GenAI solutions in a cloud - native environment at Flexera. Leading a high - impact team and driving operational excellence for ML infrastructure.
Senior ML Platform/Ops Engineer at Preply building and maintaining ML pipelines, collaborating with ML Scientists and Data Engineers.
Senior ML Platform/Ops Engineer building ML systems for AI - powered learning at Preply. Productionizing machine learning with high reliability, performance, and observability in a hybrid environment.
Senior ML Platform/Ops Engineer building AI - powered ML pipelines for a dynamic Ed - Tech company. Collaborating with ML scientists and engineers to ensure reliable deployment and observability.
Machine Learning Engineer developing advanced Deep Learning models for autonomous driving technology at Mobileye. Collaborating in a high - end algorithmic engineering team on critical computer vision challenges.
Machine Learning Engineer focusing on vulnerabilities and security of AI systems at Carnegie Mellon University. Collaborating with a team to build robust prototypes and provide solutions for government sponsors.