Manager, Generative AI

Archer; Jersey City (NJ); On-site; USD 180,000 - 240,000; Full time

Job summary

Archer, in partnership with Verisk, seeks a highly technical Manager, Generative AI to lead a globally distributed engineering team focused on production-grade GenAI products. This hands-on leadership role blends software engineering excellence with AI/ML depth to define GenAI direction and translate business opportunities into scalable solutions.

You will guide architecture, model selection, and deployment strategies while driving best practices, observability, and responsible AI across the

Qualifications

• 7+ years of industry experience in software engineering, data science, or machine learning, including technical leadership experience.
• 2+ years of hands-on experience building and delivering Generative AI/LLM solutions, preferably production applications.
• Proven experience architecting and operating large-scale production software, AI/ML systems, and/or data platforms.
• Strong understanding of LLMs, embeddings, RAG, prompt engineering, context management, and modern GenAI architectures.
• Strong Python skills and experience with modern AI/ML and GenAI frameworks and tooling.
• Experience with cloud-native architectures and production deployment of scalable applications.
• Strong understanding of GenAI evaluation, testing, monitoring, security, and responsible AI practices.
• Ability to translate business requirements into technical solutions and communicate complex technical decisions to both engineering and executive audiences.
• Bachelor's degree in Computer Science, Software Engineering, Machine Learning, Data Science, or a related technical field.

Responsibilities

• Lead and develop globally distributed engineering teams through hands-on technical leadership, performance management, mentoring, and coaching.
• Lead the architecture, design, development, and delivery of production-grade GenAI products, ensuring quality, security, scalability, reliability, performance, and cost effectiveness.
• Guide technical decisions involving LLMs, RAG, model fine-tuning/customization, prompt engineering, and agentic AI patterns.
• Establish effective approaches for GenAI evaluation, testing, observability, and production monitoring, including measurement of quality, groundedness, and reliability.
• Evaluate foundation models, AI platforms, and emerging technologies and make appropriate architecture, model-selection, and build-vs-buy decisions.
• Partner with Product Owners and business stakeholders to translate business needs into technical requirements, architectures, and delivery plans.
• Drive engineering best practices across the GenAI development lifecycle and resolve complex technical and delivery challenges.
• Communicate technical decisions, tradeoffs, project status, risks, and priorities effectively with engineering teams and senior stakeholders.

Skills

Software engineering

GenAI/LLM

Python

Leadership

Communication

Education

Bachelor's degree in Computer Science / Software Engineering / ML / Data Science

Tools

GenAI frameworks

LLM tooling

Cloud-native architectures

Job description

Archer is collaborating with Verisk to connect them with exceptional professionals for this role.

Description

Verisk ISO Corelines is seeking a highly technical Manager, Generative AI to lead a globally distributed team of engineers responsible for developing and delivering production-grade Generative AI products.

This is a hands-on technical leadership role combining strong software engineering and AI/ML expertise with people leadership. You will help define the technical direction for GenAI capabilities, translate business opportunities into scalable solutions, and lead products from concept and experimentation through production deployment.

Responsibilities

What you’ll do

• Lead and develop globally distributed engineering teams through hands-on technical leadership, performance management, mentoring, and coaching.
• Lead the architecture, design, development, and delivery of production-grade GenAI products, ensuring quality, security, scalability, reliability, performance, and cost effectiveness.
• Guide technical decisions involving LLMs, RAG, model fine-tuning/customization, prompt engineering, and agentic AI patterns.
• Establish effective approaches for GenAI evaluation, testing, observability, and production monitoring, including measurement of quality, groundedness, and reliability.
• Evaluate foundation models, AI platforms, and emerging technologies and make appropriate architecture, model-selection, and build-vs-buy decisions.
• Partner with Product Owners and business stakeholders to translate business needs into technical requirements, architectures, and delivery plans.
• Drive engineering best practices across the GenAI development lifecycle and resolve complex technical and delivery challenges.
• Communicate technical decisions, tradeoffs, project status, risks, and priorities effectively with engineering teams and senior stakeholders.

Qualifications

What you bring

• 7+ years of industry experience in software engineering, data science, or machine learning, including technical leadership experience.
• 2+ years of hands-on experience building and delivering Generative AI/LLM solutions, preferably production applications.
• Proven experience architecting and operating large-scale production software, AI/ML systems, and/or data platforms.
• Strong understanding of LLMs, embeddings, RAG, prompt engineering, context management, and modern GenAI architectures.
• Strong Python skills and experience with modern AI/ML and GenAI frameworks and tooling.
• Experience with cloud-native architectures and production deployment of scalable applications.
• Strong understanding of GenAI evaluation, testing, monitoring, security, and responsible AI practices.
• Ability to translate business requirements into technical solutions and communicate complex technical decisions to both engineering and executive audiences.
• Bachelor's degree in Computer Science, Software Engineering, Machine Learning, Data Science, or a related technical field.

Preferred Qualifications

• Hands-on experience fine-tuning or adapting LLMs, including techniques such as SFT, LoRA/PEFT, instruction tuning, or related approaches, with experience preparing training datasets and evaluating fine-tuned models against appropriate baselines.