Senior Consultant AI/ML

Role Overview

Accountabilities

• Define and own the AI/ML solution roadmap for end-to-end healthcare claims automation.
• Lead customer-facing discussions, technical workshops, and discovery sessions to prioritize agentic AI use cases.
• Design and implement scalable AI/ML architectures across claims intake, adjudication, payments, and denial management.
• Lead end-to-end delivery including technical execution, project planning, risk management, and stakeholder communication.
• Architect and implement agentic AI systems using AWS Bedrock AgentCore Runtime, Strands SDK or equivalent frameworks.
• Design agentic AI patterns including ReAct, tool use, and multi-agent collaboration.
• Develop RAG architectures using Amazon Bedrock Knowledge Bases, vector stores, and related retrieval technologies.
• Guide model development, training, validation, deployment, and optimization.
• Establish AI/ML pipelines processing millions of claims per month.
• Define and implement AI/MLOps strategies covering monitoring, retraining, drift detection, and automated testing.
• Build evaluation frameworks for agentic AI systems assessing correctness, safety, latency, and cost.
• Implement observability, tracing, monitoring, and debugging through Amazon Bedrock AgentCore Observability.

Requirements

• More than 10 years of technology delivery experience, including at least five years leading AI/ML programmes at enterprise scale.
• Extensive experience designing, developing, and deploying production-grade AI/ML solutions.
• Strong expertise in agentic AI architectures including ReAct patterns, tool calling, multi-agent orchestration, and autonomous workflow design.
• Hands-on experience with AWS AI services, particularly Amazon Bedrock AgentCore Runtime, and familiarity with the Strands SDK or equivalent frameworks.
• Demonstrated ability to design and implement RAG architectures using Amazon Bedrock Knowledge Bases, vector databases, and related retrieval technologies.
• Strong understanding of AI/ML engineering principles including model development, training, evaluation, validation, deployment, and performance optimization.
• Experience designing AI/ML and MLOps pipelines including model monitoring, drift detection, automated retraining, testing, and production operations.
• Familiarity with AI observability and evaluation practices including tracing, debugging, safety assessments, latency measurement, and cost optimization.
• Experience delivering scalable cloud-based AI solutions for high-volume enterprise workloads.
• Ability to translate complex business requirements into practical AI architectures, technical specifications, and implementation roadmaps.
• Strong customer-facing consulting skills with the ability to facilitate technical workshops and influence stakeholders.
• Demonstrated programme leadership including delivery planning, risk management, cross-functional coordination, and accountability for outcomes.

Additional Qualifications

• Knowledge of healthcare claims processing and related workflows, including claims intake, eligibility verification, medical coding, adjudication, fraud detection, payments, and denial management, is highly relevant to the role.
• Excellent analytical, problem-solving, and communication skills with the ability to balance technical depth, business priorities, and operational requirements.
• Ability to work effectively with distributed, multidisciplinary teams in a fully remote environment and manage competing priorities.

Benefits

• Fully remote work with flexibility to work remotely from anywhere.
• Contract-based opportunity contributing to a major enterprise AI/ML transformation initiative.
• Enterprise-scale impact automating healthcare claims processing at millions of claims per month.
• Hands-on exposure to agentic AI, AWS Bedrock AgentCore, Strands SDK, RAG architectures, and enterprise AI/ML operations.
• Technical leadership opportunity to shape AI/ML architecture and influence strategic technology decisions.
• Cross-functional collaboration with AI/ML engineers, data scientists, cloud architects, healthcare specialists, and business stakeholders.

Application Notes

• Applications are reviewed via an AI-powered matching process and shortlisted candidates are shared with the hiring company.
• Final interviews and assessments are managed by the hiring company's internal team.