- Company Name
- Avensys Consulting
- Job Title
- Artificial Intelligence /Machine Learning Engineer
- Job Description
-
Job Title
Artificial Intelligence / Machine Learning Engineer – Forward Deployment Engineer
Role Summary
Lead the end‑to‑end technical deployment of an agentic AI platform in enterprise environments. Design robust integration pipelines, orchestrate scalable ML model serving, automate CI/CD, and ensure security, compliance, and reliability while mentoring peers and providing field feedback to product teams.
Expectations
- Execute full technical implementation of the platform for enterprise customers.
- Deliver high‑performance, secure, and compliant ML services in production.
- Mentor junior engineers on advanced deployment, DevOps, and ML systems practices.
- Communicate effectively with customer engineering teams, architects, and executives.
Key Responsibilities
1. **Deployment Engineering** – Architect and implement integration pipelines connecting data sources, APIs, and systems of record. Deploy and scale ML models, automate using Docker, Kubernetes, Terraform, Helm, and CI/CD workflows.
2. **AI Platform Integration & Optimization** – Build custom extensions, SDKs, microservices, and scripts for data preprocessing, feature engineering, and real‑time inference. Optimize serving, caching, and resource allocation for low‑latency, high‑throughput workloads.
3. **Reliability, Security & Compliance** – Design solutions that meet enterprise‑grade resilience, observability, and scalability. Enforce encryption, identity management, network security and adhere to SOC2, HIPAA, GDPR, and other regulatory constraints.
4. **Engineering Leadership & Escalation** – Act as senior technical lead on customer deployments, resolve complex engineering challenges, and embed platform into production workflows. Provide feedback to product and core engineering teams.
5. **Enablement & Knowledge Sharing** – Create reusable deployment templates, automation scripts, and playbooks; mentor forward deployment engineers on DevOps and ML systems engineering.
Required Skills
- Strong proficiency in Python, TypeScript/JavaScript, or equivalent backend languages.
- Experience deploying ML models with TensorFlow, PyTorch, Hugging Face, or custom inference engines.
- Cloud expertise on AWS, GCP, or Azure; Kubernetes, serverless frameworks, and IaC tools (Terraform, Helm, Ansible).
- Deep understanding of API design, distributed systems, and data engineering workflows.
- DevOps expertise: CI/CD pipelines, monitoring, observability, alerting (Prometheus, Grafana, ELK, Datadog).
- Knowledge of performance profiling, scaling, and Site Reliability Engineering principles.
- Security & compliance awareness: SSO, RBAC, encryption, API security; SOC2, HIPAA, GDPR.
- Strong debugging, problem‑solving, and communication skills; ability to work under pressure with minimal guidance.
Required Education & Certifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
- Professional certifications preferred: AWS Certified Solutions Architect / Developer, Azure Solutions Architect, Google Professional Cloud Architect, Kubernetes Administrator (CKA), or equivalent.
- Certification or proven experience in ML Ops or DevOps best practices.