AI/ML & Forward Deployed Engineer

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

Key Responsibilities

1) 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.

2) 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.

3) GenAI / LLM Engineering (If Applicable)

• Build and productionize 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.

4) 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.

5) 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).

6) Technical Leadership & Enablement

• Provide technical guidance and mentoring to engineers; 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.