Build and scale production-grade AI solutions on AWS for insurance platforms
Compensation
$120,000 - $150,000 annual base salary depending on experience, qualifications, and geographic location $130,000 - $170,000 annual base salary depending on experience, qualifications, and geographic location in CA, CT, MA, NJ, NY, and PA
Responsibilities
Build AI systems
Design and implement end-to-end AI/ML solutions including LLM-based applications
Build RAG pipelines using vector databases and enterprise data sources
Build machine learning models that automate their training, validation, monitoring, and retraining
Develop APIs and services to operationalize AI capabilities across the organization
A leadership role focused on defining and owning LLM engineering standards, memory architecture, and RAG pipeline architecture across agent pods for an AI/ML platform.
Responsibilities
Define and own LLM engineering standards across all Agent Stream pods
Own the Langfuse observability framework at platform level
Standardise RAG pipeline architecture across pods
Review and challenge agent design proposals from pod AI/ML engineers
Contribute to AI/ML hiring and define technical bars
The Vice President of AI/ML Engineering for Medicare & Retirement (M&R) is responsible for driving AI-first engineering transformation across the portfolio, enabling scalable, production-grade AI capabilities that power growth, cost optimization, and operational efficiency for Medicare Advantage and Dual Eligible Special Needs Plan (DSNP) businesses.
Locations (3)
United States
Minneapolis, Minnesota, United States
Washington, D.C., District of Columbia, United States
Responsibilities
Define and execute an enterprise AI/ML strategy aligned to M&R business priorities and growth objectives
Embed AI across core value streams: benefit design, member lifecycle, and clinical operations
Drive transition from experimentation to scaled, production-grade AI across the portfolio
Build and scale enterprise AI/ML platforms supporting model development, deployment, and monitoring
A hands-on technical contributor (Generative AI) responsible for building, deploying, and maintaining production-grade AI/ML and Generative AI solutions across client and internal initiatives.
Compensation
The pay range for this position is $107500.00 - $161300.00/yr.
Responsibilities
Design, implement, test, deploy, and maintain production-grade AI/ML and Generative AI solutions.
Build and integrate LLM-powered application capabilities including summarization, Q&A, document processing, semantic search, and workflow augmentation.
Develop retrieval-augmented generation (RAG) solutions using embeddings, chunking strategies, vector databases or search, semantic retrieval, and grounding techniques.
Integrate hosted AI services and foundation models into enterprise applications through secure APIs, microservices, and cloud-native services.
Develop and maintain backend services, APIs, pipelines, and automation using Python and related frameworks.
The AI Engineer will design, develop, and deploy advanced, production-ready AI solutions, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, agentic AI workflows, and intelligent automation technologies.
Compensation
₹15,00,000 – ₹22,00,000 per year (15–22 LPA), commensurate with experience and technical expertise
Responsibilities
Design, develop, and deploy AI systems, including LLMs, RAG, agentic workflows, and ML/DL solutions.
Select appropriate models and architectures while balancing accuracy, latency, cost, and complexity.
Train and fine-tune models using PyTorch or Keras, including data preparation and evaluation.
Implement and benchmark research methods before adoption.
Build APIs, inference pipelines, and backend integrations.
Senior technical leadership role within the Enterprise AI team, focused on setting standards for AI-native development and owning delivery outcomes for strategic business units.
Lead the Forward Deployed Engineering function within Enterprise AI, proving AI ideas through PoCs, PoVs, and MVPs before handing them off for scale. This is a hands-on leadership role requiring technical depth in AI-augmented engineering.
Responsibilities
Lead the Forward Deployed Engineering function, owning strategy, structure, hiring, development, and standards.
Hire, mentor, and develop the FDE team, creating development paths from G8 through to G5.
Set the standards for AI-native engineering practice (agentic delivery-automation workflows, delivery bot meshes, prompt and pattern libraries).
Set the bar for AI, ML, and data science rigor across the function, including evaluation standards and model validation.
Own relationships with Director and Senior Director-level business unit sponsors.
Requirements
Bachelor's degree in computer science
Software Engineering
Nice to have: Prior leadership experience at an AI-native company (Anthropic, Palantir, OpenAI)
Nice to have: Master's degree in computer science or a related field
Nice to have: Experience in regulated industries (pharma, healthcare, financial services)
Support enterprise AI/ML architecture initiatives, governance frameworks, and agentic AI capabilities across federal modernization programs.
Compensation
Salary Range: $120k - $155k
Responsibilities
Support AI/ML architecture design and engineering activities across enterprise architecture and modernization initiatives.
Assist in developing and maintaining enterprise AI architecture baselines, target-state architectures, and transition roadmaps.
Contribute to solution architecture efforts for AI-enabled systems, integrating cloud-native services and advanced analytics platforms.
Support the development and maintenance of AI model governance frameworks, including model lifecycle management, risk assessments, and compliance monitoring.
Assist in designing scalable data pipeline architectures to support AI/ML training, inference, monitoring, and continuous improvement.
Requirements
Bachelor’s degree in Computer Science, Engineering, Data Science, or related field
Master’s degree in Computer Science, Engineering, Information Technology, or related discipline
Nice to have: Master’s degree
Nice to have: AI/ML platforms and services including Azure AI, Azure Machine Learning, Amazon SageMaker, OpenAI services
Nice to have: Generative AI and large language model (LLM) technologies
Define and own enterprise-level development standards for AI solutions across GSK — reference architectures, agentic delivery-automation workflows, delivery bot meshes, evaluation frameworks, and handoff hygiene.
Set the enterprise technical direction for AI-native engineering, establishing how agentic workflows and delivery bot meshes are designed, secured, monitored, and reused at scale.
Own the enterprise pattern library and reference platform, ensuring reusable agents, prompt templates, MCP integrations, and reference architectures.
Chair FDE technical design review and contribute to Enterprise AI architecture governance.
Own FDE delivery for a defined portfolio of strategic business units — engagement pipeline, prioritization, quality, and outcomes from intake to clean handoff.
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