Lead AI Safety and Enablement Engineer Job at Carex Consulting Group (Madison)

Lead AI Safety and Enablement Engineer

The Lead AI Safety and Enablement Engineer ensures the safe, reliable and scalab...

Location

United States , Madison

Salary:

147000.00 - 250000.00 USD / Year

Exact Sciences

Expiration Date

Until further notice

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field
8 years of experience in software, ML systems, or platform engineering
Practical experience with AI lifecycle tools (e.g., MLflow, Arize, WhyLabs, Label Studio)
Proficiency in Python, CI/CD processes, and cloud platforms (AWS, Azure, or GCP)
Experience developing scalable and compliant ML systems and tools
Applicants must be currently authorized to work in country where work will be performed on a full or part-time basis

Job Responsibility

Support implementation of enterprise standards for AI safety, transparency, and reliability
Develop and maintain shared AI safety tools such as model catalogs, metadata registries, and monitoring systems for bias, drift, and performance
Build APIs, templates, and SDKs that integrate governance and validation into AI/ML development pipelines
Contribute to the design of observability and telemetry solutions for continuous monitoring of model and data quality
Collaborate with Legal, Privacy, Compliance, and InfoSec teams to translate AI policies into automated controls and technical safeguards
Work with ML and GenAI teams to embed validation and safety checkpoints in AI workflows
Participate in cross-functional reviews and discussions supporting AI governance and responsible AI practices
Promote awareness and adoption of responsible AI principles through tools, documentation, and knowledge sharing
Support and comply with the company’s Quality Management System policies and procedures
Uphold company mission and values through accountability, innovation, integrity, quality, and teamwork

What we offer

Relocation assistance provided to those not local to the area and willing to relocate
Paid time off (including days for vacation, holidays, volunteering, and personal time)
Paid leave for parents and caregivers
A retirement savings plan
Wellness support
Health benefits including medical, prescription drug, dental, and vision coverage
Bonus eligible

Fulltime

Lead AI Engineer (Generative AI)

Location

Australia , Melbourne

Salary:

160000.00 - 170000.00 AUD / Year

Salt

Expiration Date

Until further notice

Requirements

2 – 4 years experience applying modern Generative AI in real delivery environments
A strong track record of providing technical leadership
A strong background in software engineering before stepping into a more AI-focused role in recent times
Deep expertise in Generative AI, large language models, retrieval‑augmented generation (RAG), and modern machine learning techniques
Strengths in prompt engineering, offline/online evaluation, safety guardrails, and telemetry‑driven improvement
Practical experience with RAG, embeddings/vector search, and tool‑use/function‑calling orchestration
The ability to define trustworthy AI metrics
A Software engineering background using the likes of React, Typescript, React Native
AWS experience / certifications
Strong knowledge of CI/CD

Job Responsibility

Accelerating the adoption of Generative AI for this company, leading the adoption of AI‑first engineering practices and an 'AI-first' mindset
Providing technical leadership and shaping AI-enabled solutions
Reporting into the Head of Technology and working closely with the Head of Enterprise AI
Collaborating with a variety of cross-functional teams including
Engineering, QA, BA, Product Management, Senior Business Stakeholders, Data Science, Cyber Security, Enterprise Architecture
Coaching and training engineers and leaders in the organisation in safe, effective AI‑assisted development
Encouraging teams to embed AI‑driven thinking in solution design and delivery
Defining and helping to operationalise trustworthy metrics to measure the impact of AI features, and developer productivity uplifts
Producing AI-augmented solution designs when required
Driving engineering excellence by uplifting standards, modernising technical practices, and championing AI‑aligned SDLC patterns

What we offer

Super
Bonus

Fulltime

Product & Solutions Lead, Safety and Security

As a Product & Solutions Lead focused on safety and security, you will build and...

Location

United States , San Francisco

Salary:

288000.00 - 425000.00 USD / Year

OpenAI

Expiration Date

Until further notice

Requirements

6+ years in product, technical program leadership, solutions, or platform operations, especially in safety, security, risk, integrity, or enterprise/public-sector contexts
Have built 0–1 solution offerings (product plus services or productized services): taking ambiguous needs, shipping something concrete, then scaling it into a repeatable model
Have a builder’s mindset: comfortable incubating early-stage ideas, testing them with partners, and evolving them into durable, repeatable safety and security solutions
Can go deep with engineers and still produce partner-ready artifacts that are clear, usable, and operationally grounded
Are strong at abstraction: turning bespoke engagements into reusable patterns, templates, and reference implementations
Have strong written communication and an instinct for clarity, precision, and partner usability

Job Responsibility

Own the 0–1 roadmap for safety and security solution offerings: define the target users, problem statements, tools, operating models, success metrics, and the set of reusable deliverables we ship
Design and ship bespoke technical solutions for priority partners (internal and external), then abstract what works into reusable patterns and toolkits
Build partner-ready technical artifacts: solution blueprints, reference architectures, evaluation and monitoring guidance, incident/response playbooks, and deployment checklists
Package open-source and proprietary capabilities into adoption-ready solutions (e.g., reference implementations, configuration patterns, validated workflows)
Maintain a consistent delivery model across engagements: intake, scoping, governance alignment, execution cadence, and retrospectives that improve the offering over time
Translate evolving threats into actionable guidance and updates for solution packages (e.g., scams/fraud patterns, cyber-enabled threats, ecosystem abuse trends)
Develop lightweight enablement components as needed: targeted technical modules, hands-on labs, and readiness assessments that accelerate adoption of the solutions
Define and instrument impact measurement: adoption milestones, readiness indicators, reliability and safety posture improvements, and partner satisfaction with outputs
Partner closely across engineering, safety, geopolitical analysis, and quantitative teams to ensure solutions are technically credible, threat-informed, and measurable
Communicate crisply and decision-readily to internal and external stakeholders: progress, trade-offs, risks, and recommendations

What we offer

Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
401(k) retirement plan with employer match
Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)
Mental health and wellness support
Employer-paid basic life and disability coverage
Annual learning and development stipend to fuel your professional growth
Daily meals in our offices, and meal delivery credits as eligible

Fulltime

Azure Apps and AI Solution Engineer

Are you curious, passionate about building intelligent applications, and ready t...

Location

Canada , Toronto

Salary:

128000.00 - 222400.00 CAD / Year

Microsoft Corporation

Expiration Date

Until further notice

Requirements

Bachelor's Degree in Computer Science, Information Technology, Engineering or related field AND 6+ years technical pre-sales or technical consulting experience OR equivalent experience
Hands-on experience designing production-grade, intelligent applications on Azure using AI Foundry, cloud-native app platform services (AKS, Container Apps, App Service), with the ability to guide customers through end-to-end AI application modernization decisions
Hands‑on experience building and shipping generative AI workloads on Azure, including RAG architectures, multi-agent orchestration, LLM engineering fundamentals (chunking/embedding strategy, hybrid search, reranking, eval-driven development), and integration with Azure AI Search
Solid understanding of end‑to‑end AI application architecture concepts, including model selection, prompt engineering, tool calling and MCP (Model Context Protocol), content safety, observability, and responsible AI practices
Experience building and deploying cloud-native applications across the Azure app platform—AKS, Container Apps, App Service, and Functions—and integrating AI capabilities via API Management, Copilot Studio, and GitHub Copilot to support intelligent, enterprise-scale workloads
Ability to guide customers through application and AI platform modernization decisions, balancing architecture, governance, cost, and performance considerations to enable AI‑ready and enterprise‑scale outcomes

Job Responsibility

Solid technical foundation designing and modernizing Cloud & AI solutions on Azure, partnering with customers to move from legacy environments to secure, scalable cloud‑native architectures
Ability to lead technical migration and modernization discussions, applying structured approaches (e.g., 6R strategy) to guide customer decisions
Experience influencing technical decision makers (architects, platform leads, engineering managers) by translating complex architecture into clear, defensible solutions
Solid understanding of hybrid and cloud‑native architectures, including networking fundamentals (virtual networks, secure connectivity, routing, performance considerations)
Knowledge of Azure security and compliance principles, including identity, networking security, data protection, and alignment to regulatory and compliance frameworks
Hands‑on technical mindset with the ability to design, validate, and explain architectures, not just describe services
Proven collaboration skills working with customers, partners, engineering teams, and account stakeholders to deliver outcomes
Commitment to continuous learning, leveraging Microsoft training resources, hands‑on labs, and certifications to deepen Cloud & AI expertise
Drive technical sales by using technical demos, proof of concepts, and technical architecture accelerators to influence solution design and enable deployments
Lead architecture sessions and technical workshops to accelerate Cloud & AI adoption

Fulltime

Head Of Infrastructure And Ai Systems

Radix is building the most trusted data and analytics platform in multifamily. J...

Location

United States , Scottsdale

Salary:

Not provided

Radix (AZ)

Expiration Date

Until further notice

Requirements

Brings 10 or more years of experience in platform, infrastructure, or systems engineering, with deep exposure to operating complex production systems rather than only building them
Demonstrates deep expertise in AWS core services, along with hands on experience in Azure, including identity federation and cross cloud integration patterns
Possesses production level experience with Kubernetes and strong proficiency in Databricks, including cluster management, scaling, security, Spark tuning, and data governance
Treats infrastructure as code as a default practice, with Terraform preferred, and builds robust observability systems across metrics, tracing, and logging with a proactive mindset
Thinks in systems, maintaining a full stack perspective from DNS through application layers to agent behavior, while identifying risks and anti patterns early
Operates with a strong Day 2 mindset, embedding reliability, operational readiness, and security as core design principles from the outset
Has real world experience integrating AI and LLM systems into production environments, with a deep understanding of agent safety, trust boundaries, observability, and failure modes
Is motivated by the challenge of enabling reliable collaboration between human engineers and AI agents, while staying current on the evolving AI infrastructure landscape
Exhibits curiosity, resilience, and adaptability when navigating ambiguity and working within fast moving, evolving systems
Thrives in high growth startup environments with strong ownership, operating with autonomy and transparency while leading through hands on execution

Job Responsibility

Architect and build the Agent Harness, the system of controls, context, and guardrails that govern how AI agents are deployed, monitored, constrained, and evolved across the platform
Define and enforce operational contracts between human engineers and AI agents, including access boundaries, audit trails, rollback mechanisms, and trust boundaries
Translate desired agent outcomes into concrete infrastructure requirements, including defining what context agents require, what systems they can interact with, and how failures are handled
Own the reliability and observability of agent driven workflows end to end, from initial invocation through downstream system impact
Evaluate, select, and integrate LLM tooling, agent orchestration frameworks, and emerging AI native infrastructure primitives as the ecosystem evolves
Own and operate all cloud infrastructure across AWS as primary and Azure, including compute, networking, storage, security posture, and cross cloud integration patterns
Architect, operate, and continuously improve Kubernetes clusters at production scale, including workload isolation, autoscaling strategies, cost optimization, and multi environment lifecycle management
Own and evolve the Databricks environment, including cluster configuration, job orchestration, access controls, and data governance, while defining clear infrastructure contracts between data engineering and the broader platform
Drive infrastructure as code practices with Terraform preferred and with a strong emphasis on repeatability, auditability, and operational simplicity
Ensure infrastructure is appropriately sized and cost efficient, intentionally architected for current team needs rather than speculative future scale

What we offer

Medical, dental and vision coverage designed to support your wellbeing
Uncapped Paid Time Off
Pre-IPO Equity
Performance Bonus
Learn From the Best
Build Category-Defining Products

Fulltime

QA Engineering Lead, AI Native

Meta is seeking a QA Engineering Lead with expertise in AI product and model tes...

Location

United States , Menlo Park

Salary:

138000.00 - 191000.00 USD / Year

Staff AI Engineer

We are looking for a visionary and technically deep AI Architect to lead the des...

Location

India , Pune, Maharashtra; Karnataka

Salary:

Not provided

Teradata

Expiration Date

Until further notice

Requirements

10+ years of experience in software engineering or technology architecture
At least 4 years focused on AI, ML, and Generative AI solutions at enterprise scale
Deep hands-on expertise with Large Language Models (LLMs) including model selection, prompt engineering, fine-tuning (LoRA, PEFT), and evaluation techniques
Proven experience designing and implementing MCP servers and structured context management strategies for LLM applications
Strong working knowledge of Generative AI frameworks and orchestration tools — LangChain, LlamaIndex, LangGraph, AutoGen, CrewAI, or equivalent agentic platforms
Hands-on experience with RAG architectures, vector databases (Pinecone, Weaviate etc), and embedding models
Proficiency in Python-based AI/ML development, including libraries such as HuggingFace Transformers, PyTorch, TensorFlow, scikit-learn, and OpenAI SDK
Experience with AI cloud platforms and services — Azure OpenAI, AWS Bedrock, Google Vertex AI, or equivalent enterprise AI infrastructure
Solid understanding of MLOps practices including model versioning, experiment tracking (MLflow, Weights & Biases), CI/CD for ML, and model monitoring in production
Familiarity with AI agent design patterns, multi-agent systems, tool-use frameworks, and autonomous workflow orchestration

Job Responsibility

Define and own the enterprise AI architecture strategy, establishing standards, reference architectures, and technology roadmaps for AI/ML adoption
Architect and oversee the design of LLM-powered applications including RAG pipelines, AI agents, and agentic workflow systems
Design and govern MCP server implementations, enabling structured, context-aware interactions between LLMs and enterprise data sources
Lead the evaluation and selection of AI/ML platforms, LLM providers (OpenAI, Anthropic, Azure OpenAI, Google Gemini, open-source models), and supporting infrastructure
Architect prompt engineering frameworks, fine-tuning pipelines, and model evaluation strategies to ensure LLM output quality, accuracy, and reliability
Design AI orchestration layers using frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, or custom agentic architectures for multi-step reasoning workflows
Establish AI data pipelines for model training, embeddings generation, vector database management, and real-time inference serving
Define and enforce responsible AI practices including bias detection, explainability, hallucination mitigation, content safety guardrails, and regulatory compliance
Collaborate with engineering leads, data scientists, and product teams to embed AI capabilities into existing platforms and new product initiatives
Mentor and upskill engineering teams on AI/ML technologies, architectural patterns, and emerging developments in the AI ecosystem

What we offer

People-first culture
Flexible work model
Focus on well-being
Inclusive environment

Fulltime

Senior Staff Software Engineer - AI

GEICO is seeking an experienced Engineer with a passion for building high-perfor...

Location

United States , Seattle, WA; Austin, TX; Palo Alto, CA; Chicago, IL; Dallas, TX

Salary:

110000.00 - 230000.00 USD / Year

Geico

Expiration Date

Until further notice

Requirements

Experience building and deploying ML systems in production with cross-functional engineering teams
Fluency in at least two modern languages such as Python, Go, Java, C++, or C# including object-oriented design
Experience architecting multi-component ML platforms using open-source/cloud-agnostic components: Datastores: PostgreSQL, NoSQL (MongoDB, Cassandra, CosmosDB) Streaming: Kafka, Flink, or Spark Streaming
Experience with end-to-end ML lifecycle: version control, CI/CD, Kubernetes, testing, monitoring, and production support
Experience with cloud providers (Azure, AWS or GCP) in production ML environments
Experience with observability tools and distributed systems monitoring, logging, tracing, and root cause analysis
Experience building multi-agent systems using LLMs and agentic frameworks (e.g., LangChain, LangGraph, AutoGen, Semantic Kernel, CrewAI)
Hands-on experience with RAG, semantic search, and vector databases (e.g., Milvus, pgvector, Qdrant, ElasticSearch)
Experience designing human-in-the-loop workflows and safety controls for autonomous systems
Strong architecture and design skills with ability to influence technical direction and roadmap

Job Responsibility

Design and build a multi-agent AI platform where specialized agents autonomously detect, diagnose, and resolve issues through agent-to-agent (A2A) collaboration
Develop intelligent agents using LLMs and agentic frameworks that coordinate detection, diagnostic, remediation, and knowledge tasks with minimal human intervention
Define agent interaction protocols, A2A communication standards, and evaluation frameworks for agent decision quality and autonomous action safety
Architect vector database solutions (Milvus, pgvector, Qdrant) for semantic search and RAG to enable context-aware agent decision-making
Build end-to-end ML pipelines for severity classification, anomaly detection, failure pattern recognition, and impact forecasting using observability data
Establish scalable orchestration infrastructure for multi-agent workflows with CI/CD, automated evaluation, canary releases, and rollback strategies
Implement monitoring for agent interactions, A2A communication patterns, decision quality, data drift, and system reliability
Lead technical architecture ensuring scalability, observability, and integration with existing alerting, logging, and monitoring systems
Define standards for agent safety, explainability, governance, and human-in-the-loop controls for high-impact automated actions
Partner with SRE, Product, and Engineering teams to translate reliability goals into measurable ML objectives and maintain pragmatic technical roadmaps

What we offer

Comprehensive Total Rewards program that offers personalized coverage tailor-made for you and your family’s overall well-being
Financial benefits including market-competitive compensation
a 401K savings plan vested from day one that offers a 6% match
performance and recognition-based incentives
and tuition assistance
Access to additional benefits like mental healthcare as well as fertility and adoption assistance
Supports flexibility- We provide workplace flexibility as well as our GEICO Flex program, which offers the ability to work from anywhere in the US for up to four weeks per year

Fulltime

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Lead AI Safety and Enablement Engineer

Job Description

Job Responsibility

Requirements

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