Career Opportunities: Manager - BIM (10831)
Position Summary
Highly skilled GenAI Application Leads with 7 to 12 years of total experience who can lead the design, development, testing, and deployment of Generative AI–based applications focused on Data and Analytics in Life Sciences domain. The ideal candidate will have a strong background in Python, RAG, knowledge graphs, Gen AI/LLM frameworks (LangChain, LangGraph), AWS/Azure cloud services with hands-on experience integrating and fine-tuning GPT, Anthropic Claude, Mistral, or Snowflake Cortex for real-world business use cases. Strong client problem-solving skills across life sciences data and analytics is a plus.
This role bridges AI engineering, data analytics, and full-stack development, creating intelligent applications that augment data-driven decision-making.
Education
Job Responsibilities
- Solution Architecture & Design
- Support the end-to-end architecture and design of Generative AI applications
- Support solution blueprints combining LLMs, Retrieval-Augmented Generation (RAG), Knowledge Graphs for structured and unstructured data sources.
- Translate business requirements into modular AI workflows, ensuring scalability, security, and performance.
- Evaluate and recommend GenAI frameworks/tools (LangChain, LangGraph, Semantic Kernel, etc.)
- Collaborate with data engineers and pharma domain experts to design semantic data models and context-aware knowledge base.
- Gen AI Application Development & Engineering
- Lead full-stack design and development using Python (FastAPI, Flask) and React/Next.js for GenAI-powered frontends.
- Build microservices or API layers that expose AI functionalities securely across teams and systems.
- Ensure robust CI/CD pipelines, version control (GitHub, Bitbucket, GitLab), and containerization (Docker, Kubernetes).
- Design and develop user-centric applications that embed GenAI outputs seamlessly into custom UI or enterprise BI tools like Power BI
- Work with data engineering and analytics teams to connect GenAI apps to existing data ecosystems (AWS S3, Azure Data Lake, Snowflake, Databricks, etc.)
- Use knowledge graphs and metadata-driven approaches to enhance contextual reasoning and data discovery
- Deploy AI workloads using Azure OpenAI, AWS Sagemaker, Bedrock, or Snowflake Cortex AI Services.
- AI Model Integration & Fine-tuning
- Lead the integration of LLMs (OpenAI GPT, Anthropic Claude, Mistral, Snowflake Cortex, etc.) into enterprise-grade applications.
- Fine-tune or prompt-tune foundation models using domain-specific data (commercial, patient, Omni -channel, clinical, or market access data).
- Design and implement RAG architectures leveraging vector databases (ChromaDB, Pinecone, FAISS, Weaviate etc.).
- Develop prompt engineering frameworks and guardrails to ensure factuality, interpretability, and compliance.
- Establish evaluation pipelines for model performance, accuracy, latency, and hallucination detection.
- Leadership & Collaboration
- Lead a cross-functional GenAI development team of engineers, business analysts, data scientists, and UI developers.
- Stay ahead of the curve with emerging LLM architectures, multi-agent systems, and reasoning frameworks to provide technical guidance to the teams.
- Drive knowledge-sharing sessions and PoCs to evangelize Generative AI adoption across the organization.
- Contribute to Gen AI use case roadmaps, thought leadership relevant to GenAI in Life Sciences.
Work Experience
Highly skilled GenAI Application Leads with 6 to 12 years of total experience who can lead the design, development, testing, and deployment of Generative AI–based applications focused on Data and Analytics in Life Sciences domain. The ideal candidate will have a strong background in Python, RAG, knowledge graphs, Gen AI/LLM frameworks (LangChain, LangGraph), AWS/Azure cloud services with hands-on experience integrating and fine-tuning GPT, Anthropic Claude, Mistral, or Snowflake Cortex for real-world business use cases. Strong client problem-solving skills across life sciences data and analytics is a plus.
Behavioural Competencies
Technical Competencies
Skills
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Position Summary
Highly skilled GenAI Application Leads with 7 to 12 years of total experience who can lead the design, development, testing, and deployment of Generative AI–based applications focused on Data and Analytics in Life Sciences domain. The ideal candidate will have a strong background in Python, RAG, knowledge graphs, Gen AI/LLM frameworks (LangChain, LangGraph), AWS/Azure cloud services with hands-on experience integrating and fine-tuning GPT, Anthropic Claude, Mistral, or Snowflake Cortex for real-world business use cases. Strong client problem-solving skills across life sciences data and analytics is a plus.
This role bridges AI engineering, data analytics, and full-stack development, creating intelligent applications that augment data-driven decision-making.
Education
Job Responsibilities
- Solution Architecture & Design
- Support the end-to-end architecture and design of Generative AI applications
- Support solution blueprints combining LLMs, Retrieval-Augmented Generation (RAG), Knowledge Graphs for structured and unstructured data sources.
- Translate business requirements into modular AI workflows, ensuring scalability, security, and performance.
- Evaluate and recommend GenAI frameworks/tools (LangChain, LangGraph, Semantic Kernel, etc.)
- Collaborate with data engineers and pharma domain experts to design semantic data models and context-aware knowledge base.
- Gen AI Application Development & Engineering
- Lead full-stack design and development using Python (FastAPI, Flask) and React/Next.js for GenAI-powered frontends.
- Build microservices or API layers that expose AI functionalities securely across teams and systems.
- Ensure robust CI/CD pipelines, version control (GitHub, Bitbucket, GitLab), and containerization (Docker, Kubernetes).
- Design and develop user-centric applications that embed GenAI outputs seamlessly into custom UI or enterprise BI tools like Power BI
- Work with data engineering and analytics teams to connect GenAI apps to existing data ecosystems (AWS S3, Azure Data Lake, Snowflake, Databricks, etc.)
- Use knowledge graphs and metadata-driven approaches to enhance contextual reasoning and data discovery
- Deploy AI workloads using Azure OpenAI, AWS Sagemaker, Bedrock, or Snowflake Cortex AI Services.
- AI Model Integration & Fine-tuning
- Lead the integration of LLMs (OpenAI GPT, Anthropic Claude, Mistral, Snowflake Cortex, etc.) into enterprise-grade applications.
- Fine-tune or prompt-tune foundation models using domain-specific data (commercial, patient, Omni -channel, clinical, or market access data).
- Design and implement RAG architectures leveraging vector databases (ChromaDB, Pinecone, FAISS, Weaviate etc.).
- Develop prompt engineering frameworks and guardrails to ensure factuality, interpretability, and compliance.
- Establish evaluation pipelines for model performance, accuracy, latency, and hallucination detection.
- Leadership & Collaboration
- Lead a cross-functional GenAI development team of engineers, business analysts, data scientists, and UI developers.
- Stay ahead of the curve with emerging LLM architectures, multi-agent systems, and reasoning frameworks to provide technical guidance to the teams.
- Drive knowledge-sharing sessions and PoCs to evangelize Generative AI adoption across the organization.
- Contribute to Gen AI use case roadmaps, thought leadership relevant to GenAI in Life Sciences.
Work Experience
Highly skilled GenAI Application Leads with 6 to 12 years of total experience who can lead the design, development, testing, and deployment of Generative AI–based applications focused on Data and Analytics in Life Sciences domain. The ideal candidate will have a strong background in Python, RAG, knowledge graphs, Gen AI/LLM frameworks (LangChain, LangGraph), AWS/Azure cloud services with hands-on experience integrating and fine-tuning GPT, Anthropic Claude, Mistral, or Snowflake Cortex for real-world business use cases. Strong client problem-solving skills across life sciences data and analytics is a plus.
Behavioural Competencies
Technical Competencies
Skills
-
- The job has been sent to
Position Summary
Highly skilled GenAI Application Leads with 7 to 12 years of total experience who can lead the design, development, testing, and deployment of Generative AI–based applications focused on Data and Analytics in Life Sciences domain. The ideal candidate will have a strong background in Python, RAG, knowledge graphs, Gen AI/LLM frameworks (LangChain, LangGraph), AWS/Azure cloud services with hands-on experience integrating and fine-tuning GPT, Anthropic Claude, Mistral, or Snowflake Cortex for real-world business use cases. Strong client problem-solving skills across life sciences data and analytics is a plus.
This role bridges AI engineering, data analytics, and full-stack development, creating intelligent applications that augment data-driven decision-making.
Education
Job Responsibilities
- Solution Architecture & Design
- Support the end-to-end architecture and design of Generative AI applications
- Support solution blueprints combining LLMs, Retrieval-Augmented Generation (RAG), Knowledge Graphs for structured and unstructured data sources.
- Translate business requirements into modular AI workflows, ensuring scalability, security, and performance.
- Evaluate and recommend GenAI frameworks/tools (LangChain, LangGraph, Semantic Kernel, etc.)
- Collaborate with data engineers and pharma domain experts to design semantic data models and context-aware knowledge base.
- Gen AI Application Development & Engineering
- Lead full-stack design and development using Python (FastAPI, Flask) and React/Next.js for GenAI-powered frontends.
- Build microservices or API layers that expose AI functionalities securely across teams and systems.
- Ensure robust CI/CD pipelines, version control (GitHub, Bitbucket, GitLab), and containerization (Docker, Kubernetes).
- Design and develop user-centric applications that embed GenAI outputs seamlessly into custom UI or enterprise BI tools like Power BI
- Work with data engineering and analytics teams to connect GenAI apps to existing data ecosystems (AWS S3, Azure Data Lake, Snowflake, Databricks, etc.)
- Use knowledge graphs and metadata-driven approaches to enhance contextual reasoning and data discovery
- Deploy AI workloads using Azure OpenAI, AWS Sagemaker, Bedrock, or Snowflake Cortex AI Services.
- AI Model Integration & Fine-tuning
- Lead the integration of LLMs (OpenAI GPT, Anthropic Claude, Mistral, Snowflake Cortex, etc.) into enterprise-grade applications.
- Fine-tune or prompt-tune foundation models using domain-specific data (commercial, patient, Omni -channel, clinical, or market access data).
- Design and implement RAG architectures leveraging vector databases (ChromaDB, Pinecone, FAISS, Weaviate etc.).
- Develop prompt engineering frameworks and guardrails to ensure factuality, interpretability, and compliance.
- Establish evaluation pipelines for model performance, accuracy, latency, and hallucination detection.
- Leadership & Collaboration
- Lead a cross-functional GenAI development team of engineers, business analysts, data scientists, and UI developers.
- Stay ahead of the curve with emerging LLM architectures, multi-agent systems, and reasoning frameworks to provide technical guidance to the teams.
- Drive knowledge-sharing sessions and PoCs to evangelize Generative AI adoption across the organization.
- Contribute to Gen AI use case roadmaps, thought leadership relevant to GenAI in Life Sciences.
Work Experience
Highly skilled GenAI Application Leads with 6 to 12 years of total experience who can lead the design, development, testing, and deployment of Generative AI–based applications focused on Data and Analytics in Life Sciences domain. The ideal candidate will have a strong background in Python, RAG, knowledge graphs, Gen AI/LLM frameworks (LangChain, LangGraph), AWS/Azure cloud services with hands-on experience integrating and fine-tuning GPT, Anthropic Claude, Mistral, or Snowflake Cortex for real-world business use cases. Strong client problem-solving skills across life sciences data and analytics is a plus.