Job description
Role Overview
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, and 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.