- Company Name
- Avance Consulting
- Job Title
- Deployment Engineer
- Job Description
-
**Job title:** Deployment Engineer
**Role Summary:**
Lead technical implementation of an AI platform in enterprise environments, designing integration pipelines, deploying and scaling ML models, and ensuring reliability, security, and compliance. Provide senior technical guidance to customer teams and drive continuous improvement of deployment tooling and practices.
**Expectations:**
* End-to-end ownership of platform deployment and scaling.
* Mastery of cloud, container, CI/CD, and infrastructure-as-code tools.
* Deep understanding of AI/ML operationalization, observability, and compliance.
* Ability to collaborate with engineering leaders and translate technical requirements into robust solutions.
**Key Responsibilities:**
* Design and implement integration pipelines linking customer data sources, APIs, and systems of record to the platform.
* Deploy, monitor, and scale ML models (TensorFlow, PyTorch, Hugging Face, custom inference).
* Automate deployments via CI/CD, Terraform, Helm, or Ansible; orchestrate with Docker/Kubernetes.
* Build custom extensions, SDKs, and microservices for data preprocessing, feature engineering, and real‑time inference.
* Optimize model serving, caching, and resource allocation for low-latency, high-throughput workloads.
* Architect resilient, observable, and scalable solutions; enforce encryption, identity management, network security, and SOC2/HIPAA/GDPR compliance.
* Serve as senior technical lead on customer deployments; troubleshoot complex engineering issues.
* Provide feedback to product and core engineering on performance, scaling, and integration needs.
* Create reusable deployment templates, automation scripts, and playbooks; mentor peers on advanced patterns.
**Required Skills:**
* 5+ years in software engineering, infrastructure engineering, or applied ML engineering.
* Proficient in Python, TypeScript/JavaScript or equivalent backend language.
* Experience deploying to AWS, GCP, or Azure using Kubernetes, serverless frameworks, and Terraform/Helm/Ansible.
* Strong API design, distributed systems, and data engineering knowledge.
* Operational experience with ML model serving (TensorFlow, PyTorch, Hugging Face).
* CI/CD pipeline design, IaC, container orchestration, and monitoring (Prometheus, Grafana, ELK, Datadog).
* Familiarity with performance profiling, scaling, and SRE principles.
* Knowledge of SSO, RBAC, encryption, API security, and compliance frameworks (SOC2, HIPAA, GDPR).
* Excellent debugging, problem‑solving, and communication with technical stakeholders.
**Required Education & Certifications:**
* Bachelor’s degree in Computer Science, Software Engineering, or related field (or equivalent experience).
* Relevant certifications (e.g., AWS Certified DevOps Engineer, Google Professional Cloud Architect, Kubernetes Administrator) are a plus.