Application Programming Interface (API), Artificial Intelligence (AI), Artificial Intelligence (AI) Agents, Automation, Best Practices, Business Case, Cloud Computing, Continuous Deployment/Delivery, Continuous Integration, Cross-Functional, Data Analysis, Design Patterns Programming Methodologies, Docker, GitHub, Google App Engine (GAE), Identify Issues, Java, MCP - Microsoft Certified Professional, Machine Learning, Mentoring, Metrics, Microsoft Product Family, Microsoft Windows Azure, Performance Modeling, Performance Tuning/Optimization, Python Programming/Scripting Language, Source Code/Configuration Management (SCM), Strategic Planning, Team Player, Test Plan/Schedule, Testing
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AI Solutions Architect
The AI Solutions Leader defines the technical direction for AI, ML, and data-driven capabilities across the enterprise.
The role shapes the architecture for intelligent, cloud-native solutions leveraging machine learning, LLMs, automation, and modern data platforms while ensuring security, scalability, compliance, and operational resilience.
Key Responsibilities
- Act as a subject matter expert and internal champion for AI capabilities.
- Hands on experience in LangChain & LangGraph 1.2.x
- Good understanding on agentic design patterns (workflows and multi agentic patterns - supervisor, sub agents and swarm)
- Writing evaluations to agentic systems. (Metric based and LLM as judge)
- Hands on experience in MCP servers and MCP clients.
- Azure AI foundry stack : nice to have
o Azure AI search : semantic, keyword & hybrid search
o Azure document intelligence
- Multi-threading in Python: nice to have
o AsyncIO
o Non blocking programming.
- Lead end-to-end solutioning of AI agents and agentic workflows, including design, development, testing, and deployment.
- Build business cases, conduct process reviews, perform data analysis, and document business requirements.
- Apply prompt and context engineering techniques to optimize AI model performance.
- Work with cutting edge Generative AI models such as OpenAI (ChatGPT), Anthropic, Microsoft, and Meta (LLaMA).
- Develop and deploy AI solutions using Python, Java, and frameworks like FastAPI, integrating with APIs and App Engine.
- Implement and manage CI/CD pipelines for efficient and secure deployment.
- Ensure secure handling of API keys, passwords, and tokens across environments.
- Use Docker for containerization and GitHub for version control and collaborative development.
- Conduct testing of AI agents and workflows to ensure reliability, accuracy, and performance.
- Collaborate with cross-functional teams to identify business problems and apply AI-driven solutions.
- Mentor Operations staff on AI solutioning, prompt design, and best practices.
- Drive AI adoption across the Operations Practice through enablement sessions, bootcamps, and strategic initiatives.
- Execute rapid response AI projects with a focus on delivery and measurable business impact.
'',''!*!
AI Solutions Architect
The AI Solutions Leader defines the technical direction for AI, ML, and data-driven capabilities across the enterprise.
The role shapes the architecture for intelligent, cloud-native solutions leveraging machine learning, LLMs, automation, and modern data platforms while ensuring security, scalability, compliance, and operational resilience.
Key Responsibilities
- Act as a subject matter expert and internal champion for AI capabilities.
- Hands on experience in LangChain & LangGraph 1.2.x
- Good understanding on agentic design patterns (workflows and multi agentic patterns - supervisor, sub agents and swarm)
- Writing evaluations to agentic systems. (Metric based and LLM as judge)
- Hands on experience in MCP servers and MCP clients.
- Azure AI foundry stack : nice to have
o Azure AI search : semantic, keyword & hybrid search
o Azure document intelligence
- Multi-threading in Python: nice to have
o AsyncIO
o Non blocking programming.
- Lead end-to-end solutioning of AI agents and agentic workflows, including design, development, testing, and deployment.
- Build business cases, conduct process reviews, perform data analysis, and document business requirements.
- Apply prompt and context engineering techniques to optimize AI model performance.
- Work with cutting edge Generative AI models such as OpenAI (ChatGPT), Anthropic, Microsoft, and Meta (LLaMA).
- Develop and deploy AI solutions using Python, Java, and frameworks like FastAPI, integrating with APIs and App Engine.
- Implement and manage CI/CD pipelines for efficient and secure deployment.
- Ensure secure handling of API keys, passwords, and tokens across environments.
- Use Docker for containerization and GitHub for version control and collaborative development.
- Conduct testing of AI agents and workflows to ensure reliability, accuracy, and performance.
- Collaborate with cross-functional teams to identify business problems and apply AI-driven solutions.
- Mentor Operations staff on AI solutioning, prompt design, and best practices.
- Drive AI adoption across the Operations Practice through enablement sessions, bootcamps, and strategic initiatives.
- Execute rapid response AI projects with a focus on delivery and measurable business impact.
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