Description

Key Responsibilities

• Design and implement agent-based AI workflows for different domains such as :
  • healthcare use cases (clinical assistants, triage agents, care pathway optimization, RCM automation)
  • Design agentic AI architectures for manufacturing workflows
  • Build multi-agent financial AI systems (analysis agents, compliance agents, advisory agents)
  • Implement RAG over financial documents, policies, contracts, and filings
  •  
• Build LLM-powered systems (RAG, tool-calling agents, multi-agent orchestration)
• Develop classical ML models (risk scoring, prediction, clustering, anomaly detection)
• Implement HIPAA aware AI architectures with auditability and traceability
• Implement GenAI systems using RAG over CRM, customer data, and content
• Build full-stack applications (Python APIs, AI services, UI dashboards)
• Integrate with EHRs, data lakes, and healthcare systems (FHIR/HL7 exposure preferred)
• Integrate LLMs with sensor data, MES, ERP, and IoT platforms
• Collaborate with SMEs to translate medical workflows into agent logic
• Deploy, monitor, and optimize AI systems in Azure

Basic Qualifications:

• 8+ Years of Strong experience in Python-based AI systems 
•  8+ Years of Hands-on experience with GenAI (LLMs, RAG, embeddings, prompt engineering)
•  8+ Years of Experience with traditional ML (classification, regression, NLP, time-series)
•  5+ Years of Full-stack experience (API design + UI integration)
• 5+ Years of Azure cloud experience (Azure ML, Azure OpenAI, Functions, AKS)
• 5+ Years of Experience working in regulated or compliance-heavy domains
  • Comfortable working with subject-matter experts
  • Strong learning mindset and adaptability
  • Experience building production AI systems, not just prototypes
  • Ability to explain AI decisions to non-technical stakeholders
  • Interest in agentic AI and next-generation AI architectures

Key Skills

Education

Bachelor's degree