Solutions Architect AI

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
• Docker, Kubernetes, CI/CD, cloud (AWS or Azure), observability
• 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)