Forward Deployment Engineer

We are looking for an experienced AI/ML & Forward Deployed Engineer with 8+ years of engineering experience to deliver high-impact AI/ML (and GenAI, where applicable) solutions end-to-end. You will blend applied machine learning, software engineering, and stakeholder problem-solving to deploy production-grade systems that are scalable, secure, observable, and aligned to business KPIs.

This role is ideal for engineers who enjoy operating at the intersection of data + models + systems + real users, and who can thrive in ambiguous, fast-moving environments.

• Use-Case Discovery & Forward Deployment
• Partner with stakeholders (business/product/customers) to identify and shape AI opportunities into well-defined use cases with success metrics, constraints, and rollout plans.
• Run workshops and technical discovery to assess feasibility, data readiness, integration needs, and operational risks.
• Drive rapid prototyping, pilot deployments, and iterative improvements based on real user feedback.
• Applied ML Engineering (Classic ML + Deep Learning)
• Develop and improve ML solutions (classification, regression, ranking, forecasting, anomaly detection, NLP).
• Establish and maintain robust evaluation practices: offline metrics, validation strategies, experimentation, and A/B testing.
• Perform feature engineering, error analysis, model optimization, and performance tuning for production requirements.
• GenAI / LLM Engineering (If Applicable)
• Build and product ionize RAG (Retrieval-Augmented Generation) pipelines, including document ingestion, chunking strategy, embeddings, retrieval tuning, reranking, and response grounding.
• Implement guardrails and reliability patterns: prompt templates, tool/function calling, hallucination reduction, citation strategies, and fallback paths.
• Develop evaluation harnesses for GenAI: quality metrics, regression tests, safety tests, and human-in-the-loop workflows.
• Productionization (MLOps / LLMOps)
• Package models into scalable services and deploy using Docker/Kubernetes and CI/CD.
• Implement model lifecycle management: model registry, versioning, automated retraining triggers, and governance workflows.
• Build monitoring and observability: drift detection, latency/throughput monitoring, error tracking, alerting, and rollback mechanisms.
• Systems Integration & Platform Collaboration
• Build integration layers (REST/gRPC APIs, event-driven services) to embed AI capabilities into products and enterprise workflows.
• Collaborate with data engineers to design reliable pipelines and ensure data quality, lineage, and governance.
• Ensure secure and compliant design (PII/PHI handling, RBAC, secrets management, encryption, audit trails).
• Technical Leadership & Enablement
• Provide technical guidance and mentoring to eng ineers; lead design reviews and establish best practices.
• Document solutions with architecture diagrams, runbooks, and operational playbooks.
• Create reusable accelerators (templates, libraries, patterns) to scale deployments across teams or customers.

• Discretionary Annual Incentive.
• Comprehensive Medical Coverage:
  Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.
• Family Support:
  Maternal & Parental Leaves.
• Insurance Options:
  Auto & Home Insurance, Identity Theft Protection.
• Convenience & Professional Growth:
  Commuter Benefits & Certification & amp;
  Training Reimbursement.
• Time Off:
  Vacation, Time Off, Sick Leave & Holidays.
• Legal & Financial Assistance:
  Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.

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