What are the responsibilities and job description for the AI Solutions Architect position at Anblicks?
Solutions Architect (Artificial Intelligence)
Location: Dallas, TX
Job Summary
We are seeking an experienced AI Solutions Architect to lead the design and implementation of enterprise AI solutions while supporting client engagements and pre-sales initiatives.
This role is responsible for working closely with clients, sales teams, and engineering teams to identify AI opportunities, define solution architectures, develop proof-of-concepts, and guide the delivery of AI-driven solutions.
The ideal candidate combines deep expertise in AI/ML technologies with strong consulting and communication skills, enabling them to translate business challenges into scalable and practical AI solutions.
Key Responsibilities
Client Engagement
AI / Machine Learning
Experience With At Least One Of The Following
Location: Dallas, TX
Job Summary
We are seeking an experienced AI Solutions Architect to lead the design and implementation of enterprise AI solutions while supporting client engagements and pre-sales initiatives.
This role is responsible for working closely with clients, sales teams, and engineering teams to identify AI opportunities, define solution architectures, develop proof-of-concepts, and guide the delivery of AI-driven solutions.
The ideal candidate combines deep expertise in AI/ML technologies with strong consulting and communication skills, enabling them to translate business challenges into scalable and practical AI solutions.
Key Responsibilities
Client Engagement
- Lead technical discovery sessions with clients to understand business challenges and identify AI opportunities.
- Conduct architecture discussions and solution workshops with client technical teams.
- Act as a trusted technical advisor to client stakeholders.
- Translate business requirements into scalable AI architectures.
- Collaborate with sales teams to qualify AI opportunities.
- Design solution architectures and technical approaches during pre-sales cycles.
- Develop technical proposals, architecture diagrams, and solution documentation.
- Build proof-of-concepts (PoCs) and technical demonstrations to validate AI use cases.
- Support RFP and proposal responses.
- Design end-to-end AI systems including:
- Data ingestion and data pipelines
- Machine learning and generative AI models
- Retrieval Augmented Generation (RAG) systems
- Model orchestration and APIs
- Deployment and monitoring frameworks
- Select appropriate technologies, tools, and cloud platforms.
- Ensure solutions meet scalability, performance, and security requirements.
- Provide architecture guidance during project delivery.
- Collaborate with engineering and data science teams to implement AI solutions.
- Review technical designs and ensure alignment with architectural standards.
- Ensure best practices across MLOps, governance, and responsible AI.
- Stay current with emerging AI technologies and industry trends.
- Contribute to internal AI solution frameworks and accelerators.
- Support development of reusable architectures and best practices.
- Mentor engineers and architects within the organization.
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
- 10 years of experience in software engineering, data engineering, or architecture roles.
- 5 years of experience designing or implementing AI/ML solutions.
- Experience in client-facing consulting or solution architecture roles.
- Strong understanding of enterprise system architecture and distributed systems.
AI / Machine Learning
- Machine Learning and Deep Learning
- Generative AI and Large Language Models (LLMs)
- Natural Language Processing (NLP)
- Retrieval Augmented Generation (RAG)
- Model deployment and evaluation
- Distributed systems architecture
- Data pipelines and data platforms
- Microservices and API-based architectures
- Integration with enterprise systems
Experience With At Least One Of The Following
- AWS
- Microsoft Azure
- Google Cloud Platform
- Python ecosystem
- PyTorch or TensorFlow
- LangChain, LlamaIndex, or similar GenAI frameworks
- Vector databases
- Databricks, Snowflake, or modern data platforms
- Docker and Kubernetes