- Company Name
- K&K Talents
- Job Title
- Forward Deployed Engineer
- Job Description
-
**Job Title**: Forward Deployed Engineer
**Role Summary**
Lead end‑to‑end deployment of an AI platform in enterprise settings. Design and build integration pipelines, deploy and scale machine learning models, automate releases with CI/CD and IaC, and ensure solutions meet reliability, security, and compliance standards. Serve as the senior technical liaison with customers and mentor junior engineers.
**Expectations**
- Deliver secure, scalable, high‑performance AI services in production.
- Resolve complex deployment challenges and provide actionable feedback to product teams.
- Mentor peers and create reproducible deployment assets.
**Key Responsibilities**
1. Design and implement integration pipelines connecting data sources, APIs, and systems of record.
2. Deploy, scale, and monitor machine learning models using Docker, Kubernetes, and serverless frameworks.
3. Automate releases with CI/CD pipelines, infrastructure‑as‑code (Terraform, Helm, Ansible).
4. Build custom extensions, SDKs, APIs, microservices, and tools for data preprocessing and inference.
5. Optimize model serving, caching, and resource utilization for low‑latency, high‑throughput environments.
6. Architect resilient systems with observability, monitoring, and alerting (Prometheus, Grafana, ELK, Datadog).
7. Enforce security best practices (encryption, identity management, network segmentation) and meet compliance frameworks (SOC2, HIPAA, GDPR).
8. Lead technical escalations, collaborating with customer engineering teams to embed AI solutions into production workflows.
9. Create reusable deployment templates, automation scripts, and playbooks to accelerate future projects.
10. Mentor Forward Deploy Engineers on deployment patterns, DevOps practices, and ML systems engineering.
**Required Skills**
- 10+ years in software, infrastructure, or applied ML engineering.
- Proficient in Python and TypeScript/JavaScript (or comparable backend languages).
- Experience deploying on AWS, GCP, or Azure with Kubernetes, serverless frameworks, Terraform, Helm, Ansible.
- Deep knowledge of API design, distributed systems, and data engineering pipelines.
- Hands‑on ML model operationalization (TensorFlow, PyTorch, Hugging Face, or custom inference engines).
- Robust CI/CD, IaC, monitoring, and observability skills (Prometheus, Grafana, ELK, Datadog).
- Security expertise: SSO, RBAC, encryption, API security, and familiarity with SOC2, HIPAA, GDPR.
- Strong debugging, problem‑solving, and stakeholder communication skills.
- Ability to thrive in fast‑moving, ambiguous environments with minimal guidance.
**Required Education & Certifications**
- Bachelor’s degree (or equivalent) in Computer Science, Software Engineering, Electrical Engineering, or related field.
- Optional certifications: AWS Certified Solutions Architect, Google Cloud Professional, Azure Solutions Architect, Docker Certified Associate, Kubernetes Certified Administrator, DevOps‑specific credentials.