Design and build machine learning systems, AI workflows, retrieval pipelines, model integrations, evaluation systems, and production-ready ML features.

Connect ML capabilities with full-stack product implementation across frontend, backend, APIs, databases, DevOps, cloud infrastructure, and customer-facing workflows.

Lead practical ML execution from experimentation through deployment, monitoring, evaluation, iteration, and long-term maintainability.

Create reliable processes for model quality, data quality, prompt and retrieval evaluation, observability, error analysis, and continuous improvement.

Work closely with product, engineering, security, and leadership to translate business needs into scalable technical solutions.

The intelligence layer of BlackIQ must be useful, reliable, secure, explainable, and deeply connected to real product workflows.

This role turns AI and machine learning ideas into production systems that customers can trust and use every day.

Strong hands-on ML engineering leadership creates long-term technical differentiation instead of short-lived demos or isolated experiments.

Strong background in machine learning engineering, AI systems, data pipelines, model deployment, retrieval, ranking, evaluation, and production ML workflows.

Full-stack engineering capability across frontend, backend, databases, APIs, DevOps, cloud infrastructure, observability, and production operations.

Hands-on mentality with the ability to write code, debug systems, deploy models, improve pipelines, evaluate quality, and lead by example.

Experience with MLOps, model monitoring, data workflows, automation, scalable deployment, LLM integrations, retrieval systems, and practical system design.

Ability to move between research-style exploration, product requirements, engineering tradeoffs, and production-grade implementation.

Remote-first working style with strong ownership, clear communication, structured thinking, and the ability to turn ambiguity into working systems.