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Vector Ai Jobs in Alaska (NOW HIRING)

Senior and Applied/Agentic AI Engineer

Minto, AK · On-site

$108K - $148K/yr

Architect stateful, memory-aware AI systems that manage long-running claims processes across ... Oversee embedding strategies, vector indexing architecture, retrieval optimization, and knowledge ...

Senior Engineer - LLMOps & MLOps

Minto, AK · On-site +1

$108K - $148K/yr

Design and execute the infrastructure for Retrieval-Augmented Generation (RAG), including vector database management (OpenSearch, Pinecone, or Azure AI Search) and semantic index optimization. Legacy ...

Senior Engineer - LLMOps & MLOps

Minto, AK · On-site +1

$108K - $148K/yr

Design and execute the infrastructure for Retrieval-Augmented Generation (RAG), including vector database management (OpenSearch, Pinecone, or Azure AI Search) and semantic index optimization. Legacy ...

Vector Ai information

What is a Vector AI?

Vector AI typically refers to professionals or technologies focused on vector-based artificial intelligence, which involves the use of high-dimensional vectors to represent data and perform machine learning tasks. These experts work on algorithms that process and analyze vector data for applications like image recognition, natural language processing, and recommendation systems. Their work is crucial in making AI systems more efficient at understanding complex patterns in large datasets. In some contexts, 'Vector AI' may also refer to companies or platforms developing such technologies.

What are the key skills and qualifications needed to thrive as a Vector AI engineer?

To thrive as a Vector AI Engineer, you need strong foundations in mathematics, machine learning, and computer science, often supported by a degree in a related field. Expertise with vector databases (such as Pinecone or FAISS), programming languages like Python, and knowledge of frameworks like TensorFlow or PyTorch are typically required. Excellent problem-solving, analytical thinking, and effective communication skills help you translate complex business requirements into scalable AI solutions. These qualifications are crucial for developing, deploying, and maintaining efficient AI systems that leverage vector search and representation for real-world applications.

What are some common challenges faced by professionals working in Vector AI roles, and how can they be addressed?

Professionals in Vector AI roles often face challenges such as managing large-scale, high-dimensional data, ensuring model scalability, and optimizing search algorithms for speed and accuracy. Collaborating closely with data engineers, software developers, and product managers is crucial to integrate AI vector solutions effectively into products. Staying updated on the latest advancements in vector databases and similarity search techniques can also be demanding, so continuous learning and participation in relevant communities are highly beneficial. Adopting best practices for model evaluation and experiment tracking can help address these challenges and drive project success.

What is the difference between Vector Ai vs Data Analyst?

AspectVector AiData Analyst
Required CredentialsTechnical certifications, programming skillsDegree in statistics, data science, or related field
Work EnvironmentTech companies, AI development teamsBusiness, finance, healthcare sectors
Industry UsageAI, machine learning, software developmentData interpretation, reporting, decision support

Vector Ai professionals focus on developing and implementing AI algorithms, requiring technical skills and programming knowledge. Data Analysts interpret data to inform business decisions, often working with statistical tools. While both roles handle data, Vector Ai is more specialized in AI technology, whereas Data Analysts focus on data insights and reporting.

What are popular job titles related to Vector Ai jobs in Alaska?

For Vector Ai jobs in Alaska, the most frequently searched job titles are:

What job categories do people searching Vector Ai jobs in Alaska look for?

The top searched job categories for Vector Ai jobs in Alaska are:

What cities in Alaska are hiring for Vector Ai jobs?

Cities in Alaska with the most Vector Ai job openings:

Infographic showing various Vector Ai job openings in Alaska as of July 2026, with employment types broken down into 74% Full Time, 24% Part Time, and 2% Contract. Highlights an 64% Physical, 3% Hybrid, and 33% Remote job distribution.

Senior and Applied/Agentic AI Engineer

York Risk Services

Minto, AK

$108K - $148K/yr

Full-time

Re-posted 10 days ago


Job description

By joining Sedgwick, you'll be part of something truly meaningful. It's what our 33,000 colleagues do every day for people around the world who are facing the unexpected. We invite you to grow your career with us, experience our caring culture, and enjoy work-life balance. Here, there's no limit to what you can achieve.

Newsweek Recognizes Sedgwick as America's Greatest Workplaces National Top Companies

Certified as a Great Place to Work

Fortune Best Workplaces in Financial Services & Insurance

Senior and Applied/Agentic AI Engineer

Job Responsibilities

Lead the architecture and delivery of enterprise-grade LLM and agentic AI systems that transform claims, risk, and operational workflows.

Define technical strategy for retrieval-augmented generation (RAG), multi-agent orchestration, and autonomous workflow automation.

Design and implement advanced agentic systems capable of planning, reasoning, tool selection, execution, reflection, and recovery.

Architect stateful, memory-aware AI systems that manage long-running claims processes across multiple touchpoints.

Build multi-agent collaboration models that coordinate coverage analysis, document validation, fraud signals, compliance checks, and decision support.

Establish orchestration frameworks that manage task routing, context persistence, structured outputs, and failure handling.

Design secure tool integration layers connecting agents to claims systems, policy platforms, data warehouses, document repositories, and external data services.

Implement deterministic guardrails, schema validation, and output verification pipelines to reduce hallucination and execution risk.

Lead development of document intelligence systems leveraging LLMs for summarization, entity extraction, discrepancy detection, and structured data reconstruction.

Define prompt engineering standards and reusable reasoning templates for consistent, domain-aware outputs.

Oversee embedding strategies, vector indexing architecture, retrieval optimization, and knowledge grounding approaches.

Design evaluation frameworks to measure reasoning depth, workflow completion accuracy, hallucination rates, latency, and cost efficiency.

Implement observability layers that track agent decisions, tool usage, retrieval effectiveness, and drift across models and prompts.

Drive optimization strategies for token efficiency, caching, batching, and inference scaling.

Ensure compliance with Responsible AI principles, enterprise governance standards, audit requirements, and regulatory constraints.

Partner with enterprise architecture, cybersecurity, and data governance teams to define secure deployment patterns.

Mentor engineers on LLM orchestration patterns, workflow decomposition, and safe agent design.

Translate executive-level business objectives into scalable AI platform capabilities.

Lead proof-of-concepts through full production deployment with measurable ROI outcomes.

Continuously evaluate emerging foundation models, orchestration frameworks, and agent tooling for enterprise readiness.

Qualifications

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Engineering, or related discipline.

7-10+ years of experience in AI engineering, machine learning systems, or distributed software architecture.

3-5+ years designing and deploying LLM-powered systems in production environments.

Demonstrated experience architecting full agentic AI systems with planning, reflection, memory, and tool execution components.

Deep expertise in RAG architectures, embedding strategies, vector databases, and retrieval optimization.

Strong experience designing multi-agent orchestration frameworks and workflow engines.

Advanced proficiency in Python and enterprise API integration patterns.

Experience building secure, scalable microservices in cloud-native environments.

Strong understanding of distributed systems, event-driven architectures, and system reliability principles.

Experience implementing structured output enforcement, guardrails, and audit logging mechanisms.

Demonstrated ability to design evaluation and benchmarking frameworks for LLM and agent reliability.

Experience operating in regulated industries such as insurance, financial services, or healthcare preferred.

Proven leadership in technical design reviews, architecture governance, and cross-functional collaboration.

Strong ability to balance innovation with enterprise risk management and operational stability.

Sedgwickis an Equal Opportunity Employer and a Drug-Free Workplace.

If you're excited about this role but your experience doesn't align perfectly with every qualification in the job description, consider applying for it anyway! Sedgwick is building a diverse, equitable, and inclusive workplace and recognizes that each person possesses a unique combination of skills, knowledge, and experience. You may be just the right candidate for this or other roles.