Experience: 10+ Years
Location: Any UST Location
Employment Type: Full-Time
We are looking for an experienced Generative AI Solution Architect to lead the design and implementation of enterprise-scale AI and GenAI solutions. The ideal candidate will have strong expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Prompt Engineering, MLOps, and Cloud Platforms, with the ability to translate business challenges into scalable AI solutions.
- Define and drive the GenAI strategy and architecture aligned with business objectives.
- Identify and implement high-impact AI use cases across business functions such as workflow automation, analytics, sustainability, and enterprise operations.
- Design end-to-end AI/ML and Generative AI solutions using enterprise architecture best practices.
- Develop AI applications using OpenAI APIs, LLMs, RAG, Agentic AI frameworks, and vector databases.
- Design scalable AI architectures on cloud platforms such as AWS, Azure, or GCP.
- Build AI-powered automation solutions, enterprise knowledge assistants, and intelligent workflows.
- Integrate AI solutions with enterprise applications using REST APIs and microservices.
- Collaborate with business stakeholders, architects, data engineers, and development teams to define and deliver AI solutions.
- Lead technical design reviews, proof of concepts (PoCs), MVP development, and production deployments.
- Implement MLOps best practices for model deployment, monitoring, governance, and lifecycle management.
- Mentor engineering teams on Generative AI technologies, architecture, and development best practices.
- Drive innovation by developing reusable AI frameworks, accelerators, and automation solutions.
- 10+ years of experience in Solution Architecture or Technical Architecture.
- Strong expertise in Generative AI, Large Language Models (LLMs), and AI solution design.
- Hands-on experience with OpenAI APIs, Prompt Engineering, Function Calling, Agents, and Retrieval-Augmented Generation (RAG).
- Experience with Model Evaluation, vector databases, embeddings, and semantic search.
- Strong programming skills in Python.
- Experience building REST APIs and microservices.
- Experience with MLOps and AI model deployment.
- Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP).
- Experience with data platforms such as Snowflake, Databricks, or similar.
- Strong analytical, communication, stakeholder management, and problem-solving skills.
- Experience with Dataiku or similar machine learning platforms.
- Knowledge of Agentic AI frameworks and AI automation platforms.
- Experience integrating with enterprise applications such as ServiceNow, Jira, Tableau, or Sigma.
- Familiarity with workflow automation and RPA tools.
- Knowledge of ESG, sustainability reporting, or enterprise analytics.
- Experience with test automation tools such as Playwright or Tosca.
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
- Cloud or AI certifications are an added advantage.
Primary Skills: Generative AI, Large Language Models (LLMs), Prompt Engineering, Python, Retrieval-Augmented Generation (RAG), Vector Databases, Model Evaluation, MLOps, OpenAI, AI Solution Architecture.
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