What are the responsibilities and job description for the Gen AI Architect position at Keypixel Software Solutions?
Gen AI Architect
Rosemont, IL (3days/Week)-HYBRID
Contract
Experience: -10 Years
Tech Stack (Core): -
- Cloud: AWS (primary deployment environment; open to alternatives)
- Data/Analytics Platform: Databricks (including native chat with data capabilities and potential agent integrations)
- Agent Frameworks: LangChain, LangGraph
- Conversational analytics patterns: Ask-questions-on-data / conversational BI approaches (agent-driven analytics and dashboards
Educational Qualifications: -
- Engineering Degree BE/ME/BTech/MTech/BSc/MSc.
- Technical certification in multiple technologies is desirable.
Skills: -
Mandatory skills
- Demonstrated experience delivering agentic AI solutions beyond prototypes, including enterprise deployment considerations.
- Strong hands-on engineering background with AWS-based deployments.
- Experience working with modern data platforms (e.g., Databricks) and integrating LLM solutions with analytics/data ecosystems.
- Ability to operate as a senior individual contributor who can define architecture and implement key pieces end-to-end.
- Excellent communication and collaboration skills with US-based stakeholders
Skills & Expertise Needed
- Agentic AI engineering: building and deploying LLM-powered agents for real business workflows.
- Agent orchestration: designing multi-step and/or multi-agent flows; managing tool use, control flow, retries, and failure handling.
- Agent memory: short-term and long-term memory patterns; conversation state; summarization and context window management.
- Enterprise data access patterns for agents: retrieval/grounding strategies; context assembly from structured and unstructured sources; performance-conscious access.
- Production deployment mindset: security, reliability, monitoring, and maintainability for enterprise-grade AI services.
- Architecture & best practices: ability to design scalable components that fit into an existing framework and can be extended by the team.
- Rapid requirements intake: quickly translating ambiguous reporting/analytics asks into implementable solutions and iterating with stakeholders.
- High autonomy: tech-lead level capability without direct people management; self-directed and able to set engineering direction for the workstream