Job Description

About the role

We are looking for a hands-on AI Architect to design and guide the delivery of secure, scalable AI solutions

across enterprise workflows. Who can connect defined business needs with practical architecture, advise teams

to choose appropriate models and platforms, and support taking solutions from discovery through production

adoption while working closely with Product Owners.

Key responsibilities

• Architecture and strategy. Assess business workflows, data readiness and existing systems. Define target

architectures and phased delivery plans, choosing among rules, traditional ML, RAG and agent-based

approaches according to the use case.

• AI agents and orchestration. Design agents with tool calling and workflow orchestration. Experience with

agent orchestration frameworks, vector databases, enterprise search, model gateways and LLM evaluation

or observability tools.

• RAG and enterprise knowledge. Architect document ingestion, parsing, chunking, embeddings, vector and

hybrid search, reranking and grounded responses with source citations. Preserve source permissions and

keep knowledge current.

• Technical leadership. Produce architecture diagrams, integration contracts and decision records. Review

designs and prototypes, mentor agent developers, and explain technical trade-offs to business, security and

engineering stakeholders.

• Model selection and economics. Evaluate models and platforms against task quality, privacy, latency,

deployment needs and total cost. Optimize token usage, context size, caching and model routing; support in

measure cost per successful task and workflow ROI.

• Legacy integration. Connect AI services to existing applications, databases, APIs, batch processes and

messaging systems.

• Governance and responsible use. Define practical AI do's and don'ts, approved usage patterns, data-handling controls and review gates. Address sensitive data, prompt injection, unreliable outputs, auditability and human oversight.
• Production readiness. Define supporting cloud or hybrid infrastructure, identity and access, networking,

secrets, deployment pipelines and observability. Establish evaluation gates, service targets, monitoring and

incident response.

Required experience and skills:

• Enterprise architecture delivery. Demonstrated ownership of production AI or ML solutions and enterprise

integrations, including architecture decisions, implementation guidance and operational outcomes.

• LLMs and traditional AI. Strong understanding of large language model behavior, prompting, context limits,

embeddings, model evaluation and fine-tuning trade-offs, alongside established ML methods and their

lifecycle.

• Agents and RAG. Hands-on experience implementing tool-enabled agents and RAG pipelines. Ability to

explain retrieval quality, grounding, tool execution, memory boundaries, failure modes and appropriate

human intervention.

• Engineering and integration. Ability to build prototypes and review production code in Python and an

enterprise language such as Java or TypeScript. Strong knowledge of APIs, distributed systems, SQL/NoSQL

data stores and asynchronous integration.

• Evaluation and security. Ability to define representative test datasets and measure answer quality, retrieval

relevance, agent task completion, latency and cost. Experience implementing access controls, privacy

protections and traceable operations.

• Business and communication. Ability to estimate business value, challenge unsuitable AI use cases, guide

cross-functional teams from proof of concept to production and ability to work in fast-paced

implementation.

Preferred experience

• Knowledge of HR processes and enterprise HR platforms such as Workday, including workflow integration,

employee-data access and approval boundaries.

• Experience in complex or regulated enterprises with legacy modernization, sensitive information and formal

architecture governance.