AI/ML Architect

Position Overview:

We are seeking a highly experienced AI/ML Architect to define, design, and govern enterprise-scale AI/ML and Agentic AI platforms. This role is responsible for architecting GenAI/LLM-powered, autonomous, and cloud-native AI systems that operate across healthcare and Revenue Cycle Management (RCM) workflows.

The AI/ML Architect will provide technical leadership and architectural direction across intelligent agents, multi-agent orchestration, NLP, predictive analytics, Big Data platforms, cloud infrastructure, APIs, and RPAensuring solutions are scalable, secure, compliant, explainable, and production-ready.

This is a hands-on architecture and strategy role, bridging business outcomes, engineering execution, and responsible AI governance.

Job Roles & Responsibilities:

AI/ML & Agentic AI Architecture

• Define end-to-end AI/ML and Agentic AI architecture for enterprise platforms.
• Architect autonomous AI systems capable of:
• Goal-based reasoning
• Multi-step decision-making
• Tool/API orchestration
• Multi-agent collaboration
• Design GenAI/LLM architectures using AWS Bedrock, Azure OpenAI, HuggingFace, LangChain, and Transformer-based models.
• Establish architectural patterns for:
• Agent memory, context management, feedback loops
• Human-in-the-loop decision governance

Safe autonomous execution AI-Driven Cloud Enablement

• Architect solutions leveraging AWS Bedrock for GenAI-powered:
• Infrastructure optimization
• Predictive scaling
• Log intelligence and anomaly detection

Enable seamless integration of AI/ML models into application and infrastructure layers via APIs

GenAI, NLP & Advanced AI Capabilities

• Architect AI solutions across:
• Natural Language Processing (NLP) clinical notes, claims text, coding, summarization, chatbots
• Computer Vision document ingestion, imaging, OCR
• Predictive analytics & recommender systems revenue forecasting, denial prediction, patient engagement
• Deep learning & reinforcement learning
• Define standards for prompt engineering, fine-tuning, RAG (Retrieval-Augmented Generation), and LLM lifecycle management.

Data, Big Data & Intelligence Platforms

• Architect enterprise data and AI intelligence platforms using:
• Spark, Hadoop, EMR, Redshift, BigQuery, Databricks, Kafka
• Design real-time and batch pipelines feeding AI agents with:
• Logs, metrics, events
• Structured & unstructured healthcare and RCM data
• Enable continuous learning pipelines and reinforcement loops for AI agents and models.

Cloud-Native & Platform Architecture

• Define cloud-native AI architectures across:
• AWS (Bedrock, SageMaker, Lambda, EC2, EKS)
• Azure (OpenAI, Azure ML)
• GCP (AI Platform)
• Design microservices and API-first architectures, leveraging .NET Core APIs as AI/agent control planes.
• Establish deployment standards using:
• Docker, Kubernetes
• Serverless architectures
• CI/CD and DevOps pipelines

AgentOps, MLOps & Platform Governance

• Define AgentOps / MLOps frameworks covering:
• Model, agent, prompt, and tool versioning
• Monitoring, observability, and drift detection
• Safe rollout, rollback, and experimentation strategies
• Architect auditability and explainability into AI and agent workflows.

Ensure AI systems meet enterprise reliability, scalability, and resilience standards Automation, RPA & Orchestration

• Architect integration between AI agents and RPA platforms (UiPath, Automation Anywhere).
• Enable AI-driven orchestration of:
• Bots
• Scripts
• Cloud operations

Support hybrid automation where AI agents coordinate with human approvals Security, Compliance & Responsible AI

• Define AI governance and security architecture, ensuring:
• HIPAA, GDPR, SOC 2 compliance
• Secure model access, data isolation, and role-based controls
• Establish guardrails for:
• Ethical AI
• Bias mitigation
• Explainable and auditable decision-making
• Oversee secure deployment of AI models and agents in regulated healthcare environments.

US Healthcare & RCM Domain Enablement

• Architect AI solutions supporting:
• Claims processing
• Coding & billing automation
• Denial prediction and management
• Payment posting and revenue forecasting

Ensure architectures align with US healthcare data standards, workflows, and compliance requirements

Leadership & Strategic Influence

• Act as the AI/ML architectural authority, guiding engineers, data scientists, and platform teams.
• Partner with product, cloud, security, and business leaders to align AI strategy with business outcomes.
• Mentor senior engineers and contribute to architecture reviews, reference designs, and best practices.

Drive innovation through research, POCs, whitepapers, and AI thought leadership

Candidate Requirements:

• Bachelors or Masters degree in Computer Science, AI, Data Science, or related field.
• 812+ years of experience in AI/ML engineering, data platforms, and cloud architecture.
• 4+ years in AI/ML architecture or technical leadership roles.
• Proven experience designing GenAI, NLP, LLM-based, and Agentic AI systems.
• Strong background in US Healthcare and RCM platforms.
• Hands-on experience with multi-agent systems, autonomous AI, and AI-driven automation.

Technical Expertise:

• Agentic AI, autonomous systems, multi-agent orchestration
• GenAI & LLM stacks: Transformers, HuggingFace, LangChain, RAG, fine-tuning, prompt engineering
• AI/ML frameworks: TensorFlow, PyTorch, Keras, scikit-learn
• Big Data & Streaming: Spark, Hadoop, EMR, Redshift, BigQuery, Databricks, Kafka
• Cloud platforms: AWS, Azure, GCP (AI/ML services)
• APIs & microservices: .NET Core, REST, event-driven architectures
• RPA & automation platforms
• DevOps, CI/CD, Kubernetes, Docker
• AI governance, security, and compliance frameworks.

Skillset:

• Strong architectural and systems-thinking mindset
• Ability to translate complex AI concepts into business-aligned solutions
• Executive-level communication and stakeholder engagement
• Leadership, mentorship, and influence across large teams
• Passion for autonomous AI platforms and healthcare transformation

Strategic Impact:

• Establish enterprise AI/ML and Agentic AI platforms
• Enable autonomous, self-healing, and intelligent cloud operations
• Position AI agents as first-class platform components
• Drive scalable, compliant, and responsible GenAI adoption in healthcare & RCM

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