AI Architect AWS Bedrock ( Life Sciences)

Job Description

Job Description

AI / GenAI Architecture

• Design and implement  GenAI-powered solutions using:
• • AWS Bedrock (foundation models, agents, RAG architectures)
  • Databricks Lakehouse & Genie (semantic layer, AI-assisted querying)
  • Enterprise Copilot solutions integrated into business workflows
• Define  reference architectures for:
• • Conversational analytics platforms
  • Intelligent commercial insights assistants
  • Automated reporting and summarization
  • Next-best-action and recommendation engines

Data & Platform Engineering

• Architect  end-to-end data pipelines integrating:
• • IQVIA data (Xponent, LAAD, etc.)
  • Symphony datasets
  • Patient and claims data (open & closed claims)
  • Veeva CRM data
  • Digital marketing and engagement platforms
• Enable  data harmonization, semantic modeling, and AI-ready datasets
• Leverage  Databricks (Delta Lake, Spark)and  AWS-native services

Advanced Analytics & AI Use Cases

• Drive high-value use cases such as:
• • HCP targeting, segmentation, and profiling
  • Omnichannel engagement optimization
  • Sales force effectiveness analytics
  • Patient journey and adherence insights
  • Market access and payer analytics
• Develop  RAG-based solutions combining structured and unstructured data
• Build  AI copilots for business users (brand teams, field force, market access)

Governance, Security, and Compliance

• Ensure compliance with:
• • HIPAA and global data privacy regulations
• Implement  Responsible AI practices
• Define  data security, lineage, and access governance frameworks

Stakeholder Engagement

• Collaborate with:
• • Commercial stakeholders (Sales, Marketing, Market Access)
  • Data engineering and analytics teams
  • Enterprise architecture teams
• Translate business needs into  AI solution roadmaps and architectures
• Support  solutioning, proposals, and client engagements

Required Qualifications

Experience

• 10–15+ years in  data, analytics, or AI architecture
• 5+ years in  Life Sciences Commercial domain
• Hands-on experience with:
• • IQVIA, Symphony, Claims and Patient datasets
  • Veeva CRM and commercial data ecosystems

Technical Skills

• AI/GenAI Platforms:
• • AWS Bedrock (mandatory)
  • Databricks (Lakehouse, ML, Genie)
  • Enterprise Copilot frameworks
• Programming:
• • Python, PySpark, SQL
• AI Techniques:
• • LLMs, RAG architectures, embeddings, vector search
  • NLP and conversational AI systems
• Cloud:
• • Strong expertise in  AWS ecosystem

Architecture Expertise

• Enterprise AI architecture design
• Data modeling (semantic layers, ontologies)
• API-based integration and scalable microservices design

Preferred Qualifications

• Experience building  AI copilots or conversational assistants at scale
• Familiarity with:
• • Salesforce Data Cloud / Data 360
  • Digital marketing platforms
• Experience with  LLMOps / MLOps frameworks
• Knowledge of  patient analytics and real-world data (RWD/RWE)

Leadership & Soft Skills

• Strong executive communication skills
• Ability to influence cross-functional stakeholders
• Experience leading architecture reviews and governance forums
• Outcome-driven mindset focused on business impact

Key Deliverables

• AI/GenAI architecture blueprints
• Production-grade AI solutions
• Commercial AI use case roadmap
• Reusable accelerators (RAG pipelines, copilots, semantic layers)

Founded in 2015 and backed by two decades of hands-on experience, our strong insight on the enterprise ecosystem and its dynamics helps to offer a complete end-to-end solution. We automate recruitment processes with intelligent solutions to save time and money. Synkriom leads the industry with experience authentication of candidates.

Company Description

Founded in 2015 and backed by two decades of hands-on experience, our strong insight on the enterprise ecosystem and its dynamics helps to offer a complete end-to-end solution. We automate recruitment processes with intelligent solutions to save time and money. Synkriom leads the industry with experience authentication of candidates.