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Artificial Intelligence Engineer

MobiusEngine
United States
Full-time
AI tools:
PyTorch
TensorFlow
Applications go directly to the hiring team

Full Description

Our founder: https://www.linkedin.com/in/agrawalashwin/

Our company: https://www.linkedin.com/company/mobiusservices/

What You’ll Do

* Design, implement & ship REST/GraphQL or gRPC micro-services in Python (FastAPI/Flask) or Node.js.

* Build and deploy ML pipelines: data ingestion, feature engineering, model training & inference.

* Package models with Docker, automate releases with CI/CD (GitHub Actions / GitLab).

* Tune performance (latency, throughput, memory) and monitor services in AWS or GCP.

* Collaborate with front-end, product and data teams; participate in code reviews & architecture discussions.

What You’ll Learn

* MLOps best practices: model versioning, A/B rollout, feature stores.

* Cloud-native patterns (Kubernetes, serverless functions, message queues).

* Observability stacks (Prometheus, Grafana, OpenTelemetry).

* Secure API design and zero-downtime deployments.

Must-Have Qualifications

* Final-year B.S./B.E. (or equivalent) in CS, EE, Data Science, or related (graduating 2026).

* Solid coding skills in Python plus one of: JavaScript/TypeScript, Go, or Java.

* Coursework/projects in algorithms, data structures, databases, and at least one ML model (share repo/demo).

* Familiarity with Git and Linux CLI; clear written & verbal English.

Nice-to-Have

* Experience with PyTorch or TensorFlow.

* Knowledge of SQL & NoSQL (PostgreSQL, BigQuery, DynamoDB).

* Exposure to container orchestration (K8s, ECS) or IaC (Terraform, Pulumi).

* Understanding of data privacy, security, and API authentication (OAuth2, JWT).

Internship Details

Length

12 months (full-time preferred; ≥ 30 hrs/week)

Location

Remote-first; optional San Mateo, CA office seat

Start Date

July 2025 (flexible ± 1 month)

Stipend

Competitive monthly stipend

Path to FTE

High performers earn a full-time offer on graduation

Hiring Process

1. Technical Online Assessment (90 min coding + ML/DB MCQs).

2. Virtual Interviews (1–2 rounds with AI Lead & Engineering Manager).

3. Decision & offer in ≤ 1 week.

Applications go to the hiring team directly
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