What Skills Are Expected

• AI/ML Solution Architecture:

  Extensive experience in designing and architecting AI or machine learning solutions in an enterprise context.

• Deep Technical Knowledge:

  Strong understanding of machine learning and AI techniques, especially Generative AI and large language models.

• Multi-Agent System Design:

  Knowledge of multi-agent system patterns and frameworks.

• Prompt Engineering & RAG:

  Ability to craft effective prompts and chaining strategies for LLMs, familiar with retrieval-augmented generation methods.

• AI Ethics & Responsible AI:

  Strong grasp of AI ethics and safety principles, able to identify ethical risks and design mitigations.

• Cloud & Distributed Systems:

  Deep understanding of cloud architecture and dis tributed system design.

• Data Management:

  Solid understanding of data architecture as it relates to AI, including data pipelines, databases, and data lakes.

• Leadership & Communication:

  Excellent communication and stakeholder management skills, capable of leading discussions with C-level executives and technical brainstorming with engineers.

• Consulting and Domain Acumen:

  Prior consulting or client-facing experience, adept at requirement gathering and crafting proposals.

• Problem-Solving & Innovation:

  Creative mindset to devise innovative solutions leveraging AI agents, strong problem-solving skills.

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Continuous Learning:

Demonstrated habit of continuous learning, staying updated via research papers, conferences, or hands-on experimentation.

Key Technology Capabilities

• AI & ML Frameworks:

  Familiarity with major AI/ML frameworks and services, including OpenAI GPT models, Google PaLM/Vertex AI, and Hugging Face Transformers library.

• SaaS AI & Data Platforms:

  Experience with leading SaaS AI & Data platforms in terms of agentic AI development, implementation, orchestration, AI guardrails

• Agentic AI Tooling:

  Exposure to frameworks and libraries for building AI agents and chains, such as LangChain ,Microsoft's Semantic Kernel.

• Retrieval Systems:

  Strong knowledge of search and retrieval technologies, including vector databases and semantic search.

• Cloud Services:

  Expertise in cloud ecosystems (AWS, Azure, GCP), including cloud AI services, serverless computing, containerization, and related DevOps tools.

• Programming & Scripting:

  Proficiency in programming languages commonly used for AI and integration, primarily Python and at least one general-purpose language.

• Data Platforms:

  Knowledge of modern data platforms, including relational databases, NoSQL stores, and data processing frameworks.

• Integration & APIs:

  Experience designing and using APIs and middleware, knowledge of event-driven architectures and message brokers.

• DevOps & MLOps:

  Familiar with CI/CD pipelines and infrastructure as code, understanding of MLOps principles and tools.

• Security & Compliance Tools:

  Comfort with technologies for securing AI applications, including identity and access management, encryption, and compliance tools.

• Collaboration & Design:

  Proficient with tools used in architecture and design documentation, including UML design tools and agile project management tools.

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Emerging Tech:

Awareness of emerging tech such as knowledge graphs and reinforcement learning frameworks.

Pay:

$170,000.00 - $210,300.00 per year

Work Location:

In person