AI Architect - Pune

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

Credence Resource Management v2.1 Effective – January 20, 2026

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

Credence Resource Management v2.1 Effective – January 20, 2026

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

Credence Resource Management v2.1 Effective – January 20, 2026

 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

Skills: ml,agenticai,ai,rcm,llm,revenuecyclemanagement,genai,us healthcare