AI Engineer

Join BeamData as an AI Engineer and help build enterprise-grade AI solutions that drive real impact across finance, healthcare, retail, and emerging industries. We're looking for a builder who thrives in high-autonomy environments, loves shipping quickly, and wants ownership in a fast-growing AI/data solutions company.

In this role, you'll design and scale the backend systems that power our AI implementations for enterprise clients. From day one, you'll own core technical decisions and build the infrastructure that handles production ML pipelines, model deployments, and complex AI integrations. You'll work across the full stack, from data engineering to AI systems architecture, but your primary focus will be backend engineering and production-grade AI infrastructure.

We work with leading enterprises solving real challenges: building recommendation systems, automating loan screening, implementing ML-driven document processing, and deploying AI solutions in regulated industries. You'll ship features and systems that directly impact how our clients operate, with a strong emphasis on reliability, scalability, and production excellence.

What You'll Do

Production AI Engineering (Build, Deploy, Run)

• Design and implement production-grade AI services and pipelines (batch and real-time) with a focus on reliability, performance, and operational excellence.
• Own the deployment and scaling of models and AI solutions as secure, auditable services (APIs, jobs, workflows) with clear SLAs, monitoring, and runbooks.
• Manage complex problem resolution across environments, including production incident response and optimization.
• Build systems that handle demanding workloads: real-time recommendations, automated decision-making, document processing at scale, agentic AI systems.

Governance-by-Design (Enterprise & Compliance)

• Embed AI governance directly into engineering workflows for financial services and regulated industries, including:
• Security, access controls, and data classification
• Model risk management and audit readiness
• Privacy, consent, and responsible AI principles
• Full auditability and regulatory traceability
• Partner with Risk, Compliance, and Architecture teams to ensure solutions meet internal and external regulatory expectations
• Design systems that are audit-ready from day one

Enterprise MLOps / LLMOps

• Implement automated ML delivery pipelines covering training, evaluation, approval, deployment, and rollback
• Establish standards for model versioning, reproducibility, environment isolation, and controlled releases
• Build platforms that reduce time-to-production while increasing safety, repeatability, and governance

Cross-Domain AI Implementation

• Work on diverse AI use cases: recommendation engines, classification systems, generative AI applications, and real-time decision systems
• Understand how to adapt AI solutions across industries including customer service, e-commerce, retail, finance, healthcare, news media and entertainment
• Design systems flexible enough to power multiple customer implementations simultaneously

What We're Looking For

Required:

• 3+ years of cloud engineering experience
• 3+ years engineering experience on AI/ML and agentic AI systems
• Expert-level proficiency in Python, SQL, and cloud infrastructure (Azure, GCP, or AWS)
• Hands-on experience deploying and operationalizing AI solutions in production
• Deep understanding of production concerns: reliability, scalability, observability, cost, and security
• Ability to formalize business and user requirements into AI product features
• Familiarity with model risk management and regulatory review processes

Nice to Have:

• Degrees in Computer Science, Engineering, related disciplines or equivalent practical experience
• Experience with MLOps/LLMOps platforms and tooling
• Experience designing platform-level AI capabilities, not just individual models
• Exposure to governance frameworks and compliance requirements in banking/finance
• Experience building backend systems that integrate with multiple external APIs
• Contributions to open-source ML/data projects
• Experience delivering AI solutions in regulated industries (financial services, healthcare, insurance)

Work Location: Remote