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 RPA—ensuring 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: Bachelor’s or Master’s 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

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