Description :

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 :

1. Goal-based reasoning.

2. Multi-step decision-making.

3. Tool/API orchestration.

4. Multi-agent collaboration.

- Design GenAI/LLM architectures using AWS Bedrock, Azure OpenAI, HuggingFace, LangChain, and Transformer-based models.

- Establish architectural patterns for :

1. Agent memory, context management, feedback loops.

2. Human-in-the-loop decision governance.

3. Safe autonomous execution

AI-Driven Cloud Enablement :

- Architect solutions leveraging AWS Bedrock for GenAI-powered :

1. Infrastructure optimization.

2. Predictive scaling.

3. 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 :

1. Natural Language Processing (NLP) clinical notes, claims text, coding, summarization, chatbots.

2. Computer Vision document ingestion, imaging, OCR.

3. Predictive analytics & recommender systems revenue forecasting, denial prediction, patient engagement.

4. 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 :

1. Logs, metrics, events.

2. 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 :

1. AWS (Bedrock, SageMaker, Lambda, EC2, EKS).

2. Azure (OpenAI, Azure ML).

3. GCP (AI Platform).

- Design microservices and API-first architectures, leveraging .NET Core APIs as AI/agent control planes.

- Establish deployment standards using :

1. Docker, Kubernetes.

2. Serverless architectures.

3. 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 :

1. Bots.

2. Scripts.

3. Cloud operations.

- Support hybrid automation where AI agents coordinate with human approvals

Security, Compliance & Responsible AI :

- Define AI governance and security architecture, ensuring :

1. HIPAA, GDPR, SOC 2 compliance.

2. Secure model access, data isolation, and role-based controls.

- Establish guardrails for :

1. Ethical AI.

2. 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 :

1. Claims processing.

2. Coding & billing automation.

3. Denial prediction and management.

4. 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.

- 8- 12 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.