Southern Arkansas University
Solutions Architect AI
Southern Arkansas University, San Francisco, California, United States
Solutions Architect, AI and Deep Learning Systems (Python, Production ML)
Optional junior track:
Associate Solutions Architect (New Grad)
Location - San Francisco Bay Area (Hybrid). Remote in the US may be considered for exceptional candidates.
Why EI EI is building the next generation AI automation company for regulated scientific workflows. We help biopharma, food safety, and analytical labs move from manual expert work to production grade automation that is traceable, reliable, and deployable inside real enterprise environments.
We are not an LLM wrapper Our core is
LSM (Limited Sample Model) , a next frontier AI approach designed for low data, high stakes environments where accuracy, audit readiness, and reproducibility matter.
Role Summary This is a
deeply technical, hands on
Solutions Architect role. You will combine systems thinking with real AI engineering. You will design customer deployments, lead pilots, build integrations, and also work directly with model and data pipelines to ensure performance and reliability in production. If you can write code, debug data, reason about model behavior, and drive adoption with customers, you will thrive here.
What You Will Do
Lead technical discovery: workflows, constraints, success criteria, and rollout plans
Design end to end architectures: data ingestion, feature or embedding pipelines, model execution, evaluation, audit trails, and user workflows
Support customer deployments across cloud and hybrid environments: identity, networking, security, and observability
Troubleshoot model failures and deployment failures: data quality, drift, edge cases, latency, throughput, and correctness
Provide product feedback that improves the platform, based on real customer friction
Deep Learning and AI Skills We Need You should be strong in several of the following:
Deep learning fundamentals: optimization, generalization, loss functions, regularization, calibration
Hands on training and inference: PyTorch or JAX, GPU workflows, batching, latency and throughput tuning
Evaluation: metrics, robust validation design, error analysis, confidence and uncertainty, regression testing for ML
Data centric ML: labeling strategies, weak supervision, active learning intuition, dataset debugging
ML systems: model packaging, deployment patterns, monitoring, reproducibility, and rollback strategies
What Success Looks Like In the first 30 days
Learn EI platform architecture, LSM core concepts, and existing deployment patterns
Shadow customer calls and understand the top technical bottlenecks
In 60 days
Lead a pilot end to end and deliver measurable outcomes
Ship a reusable deployment template or integration that reduces time to value
In 90 days
Own multiple customer deployments and influence product and model roadmap
Establish a repeatable technical playbook for scaled rollouts
Minimum Qualifications
Solid system design fundamentals and ability to architect end to end solutions
Hands on experience with
ML and deep learning
through projects, research, or production work
Comfortable moving between code, data, models, and customer environments
Strong communication and high ownership
Preferred Qualifications
PyTorch or JAX proficiency
Experience integrating with enterprise systems
Biology, biochemistry, analytical chemistry, chromatography, mass spectrometry, or lab automation familiarity
Compensation and Benefits
Competitive base salary, meaningful equity, and strong upside
High impact role with direct ownership of customer outcomes
Health benefits and flexible time off (details shared during process)
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Associate Solutions Architect (New Grad)
Location - San Francisco Bay Area (Hybrid). Remote in the US may be considered for exceptional candidates.
Why EI EI is building the next generation AI automation company for regulated scientific workflows. We help biopharma, food safety, and analytical labs move from manual expert work to production grade automation that is traceable, reliable, and deployable inside real enterprise environments.
We are not an LLM wrapper Our core is
LSM (Limited Sample Model) , a next frontier AI approach designed for low data, high stakes environments where accuracy, audit readiness, and reproducibility matter.
Role Summary This is a
deeply technical, hands on
Solutions Architect role. You will combine systems thinking with real AI engineering. You will design customer deployments, lead pilots, build integrations, and also work directly with model and data pipelines to ensure performance and reliability in production. If you can write code, debug data, reason about model behavior, and drive adoption with customers, you will thrive here.
What You Will Do
Lead technical discovery: workflows, constraints, success criteria, and rollout plans
Design end to end architectures: data ingestion, feature or embedding pipelines, model execution, evaluation, audit trails, and user workflows
Support customer deployments across cloud and hybrid environments: identity, networking, security, and observability
Troubleshoot model failures and deployment failures: data quality, drift, edge cases, latency, throughput, and correctness
Provide product feedback that improves the platform, based on real customer friction
Deep Learning and AI Skills We Need You should be strong in several of the following:
Deep learning fundamentals: optimization, generalization, loss functions, regularization, calibration
Hands on training and inference: PyTorch or JAX, GPU workflows, batching, latency and throughput tuning
Evaluation: metrics, robust validation design, error analysis, confidence and uncertainty, regression testing for ML
Data centric ML: labeling strategies, weak supervision, active learning intuition, dataset debugging
ML systems: model packaging, deployment patterns, monitoring, reproducibility, and rollback strategies
What Success Looks Like In the first 30 days
Learn EI platform architecture, LSM core concepts, and existing deployment patterns
Shadow customer calls and understand the top technical bottlenecks
In 60 days
Lead a pilot end to end and deliver measurable outcomes
Ship a reusable deployment template or integration that reduces time to value
In 90 days
Own multiple customer deployments and influence product and model roadmap
Establish a repeatable technical playbook for scaled rollouts
Minimum Qualifications
Solid system design fundamentals and ability to architect end to end solutions
Hands on experience with
ML and deep learning
through projects, research, or production work
Comfortable moving between code, data, models, and customer environments
Strong communication and high ownership
Preferred Qualifications
PyTorch or JAX proficiency
Experience integrating with enterprise systems
Biology, biochemistry, analytical chemistry, chromatography, mass spectrometry, or lab automation familiarity
Compensation and Benefits
Competitive base salary, meaningful equity, and strong upside
High impact role with direct ownership of customer outcomes
Health benefits and flexible time off (details shared during process)
#J-18808-Ljbffr