AI Solutions Architect

Role & responsibilities

1. Pre-Sales Technical Leadership

• Act as the primary technical advisor during AI/ML and Generative AI sales engagements.

• Partner with sales, account management, and business development teams to qualify opportunities.

• Conduct customer discovery workshops to understand:

• Business goals

• Current technology stack

• Data maturity

• Security and compliance requirements

• Operational constraints

• Translate customer requirements into scalable AI/ML solution architectures.

• Lead technical responses for:

• RFPs/RFIs

• Proposals

• Statements of Work (SOWs)

• Technical estimations

• Present AI solution strategies, demos, proof-of-concepts, and architecture walkthroughs to technical and executive stakeholders.

• Communicate business value, ROI, implementation risks, and trade-offs to both technical and non-technical audiences.

2. AI/ML & Generative AI Solution Architecture

• Design end-to-end AI/ML and Generative AI systems from concept through production deployment.

• Define:

• Data pipelines

• Feature engineering strategy

• Model architecture

• Vector database design

• RAG (Retrieval-Augmented Generation) frameworks

• Agentic AI workflows

• Integration patterns

• Architect scalable cloud-native AI solutions on platforms such as:

• Amazon Web Services

• Microsoft Azure

• Google Cloud Platform

• Evaluate and recommend appropriate:

• LLM providers

• AI tooling

• Frameworks

• Infrastructure

• Build-vs-buy approaches

• Ensure solutions align with enterprise security, governance, and compliance standards.

3. Customer Engagement & Delivery Oversight

• Work closely with customer engineering and product teams post-sale.

• Lead architecture reviews and technical decision-making workshops.

• Produce:

• Architecture diagrams

• Technical design documents

• Decision records

• Deployment strategies

• Guide implementation teams through:

• Development milestones

• Technical blockers

• Design evolution

• Performance optimization

• Support successful production deployment and operational readiness.

4. Technical Strategy & Innovation

• Stay current with advancements in:

• Generative AI

• LLMs

• AI agents

• MLOps/LLMOps

• Cloud AI services

• Contribute to internal AI best practices, reusable accelerators, and reference architectures.

• Evaluate emerging frameworks such as:

• LangChain

• LangGraph

• Provide thought leadership through:

• Technical blogs

• Whitepapers

• Conference presentations

• Internal enablement sessions

Preferred candidate profile

Technical Expertise

The ideal candidate should possess:

• 10+ years of experience in software engineering, AI/ML, and enterprise solution architecture.

• Deep hands-on expertise in:

• Machine Learning

• Generative AI

• LLM applications

• RAG systems

• Agentic workflows

• Strong practical experience with cloud AI ecosystems including:

• Amazon Web Services Bedrock

• Microsoft Azure OpenAI

• Google Cloud Platform Vertex AI

• Strong understanding of:

• Distributed systems

• APIs and integrations

• Cloud-native architectures

• Scalable backend systems

• Security and governance

Customer & Consulting Capability

The preferred candidate should demonstrate:

• Excellent communication and presentation skills.

• Strong stakeholder management capabilities with experience engaging:

• Engineering teams

• Product leadership

• Senior executives

• VP/C-suite stakeholders

• Ability to simplify complex AI concepts into business outcomes.

• Strong consulting mindset with structured problem-solving skills.

• Experience leading customer workshops and technical strategy sessions.