AI engineer

PT Multi Prima Resources; Jakarta Utara; On-site; IDR 240,000,000 - 360,000,000; Full time

Job summary

PT Multi Prima Resources is seeking a candidate with strong fundamentals to develop and maintain a local AI system. Proficiency in Python, OOP, NumPy, and Pandas is required, with familiarity in virtual environments and pip for package management.

Knowledge of LangChain, PyTorch/TensorFlow, and vector stores (Pinecone, ChromaDB, Weaviate) is valued. Cloud platforms and MLOps basics are a plus, along with API development and Git version control.

Qualifications

• Fundamentals to develop and maintain a local AI system.
• Strong grasp of Python including core syntax and data structures.
• Familiarity with libraries for data manipulation (NumPy, Pandas).
• Experience with virtual environments and package management (pip).
• Knowledge of AI frameworks and RAG tooling is a plus.

Responsibilities

• Follow the 25 Implementation completion checklist with status, owner, evidence link and acceptance date in the project tracker.
• Ensure gate tests and related documentation are completed as part of the release process.

Skills

Python

OOP

NumPy

Pandas

Virtual environments

Pip

Tools

LangChain

PyTorch

TensorFlow

ChromaDB

Weaviate

Pinecone

Git

Job description

Candidate should have the fundamentals to develop and maintain a local AI system.Python: As the de facto language for AI and machine learning, a strong grasp of Python is essential. This includes:

• Core Python syntax and data structures
• Object-Oriented Programming (OOP) concepts
• Working with libraries like NumPy and Pandas for data manipulation and analysis
• Understanding virtual environments and package management (pip)

Generative AI Frameworks and Libraries

LangChain: This framework is crucial for developing applications powered by large language models. It enables chaining together different components to build more complex use cases, such as:

• Prompt management and optimization
• Retrieval-Augmented Generation (RAG) pipelines
• Agent development and tool integration
• Memory management for conversational AI

PyTorch / TensorFlow: While not strictly necessary for all generative AI roles, understanding these deep learning frameworks allows for deeper customization, model development, and research.

Other Relevant Tools and Concepts

Vector Databases / Vector Stores: Essential for efficient storage and retrieval of embeddings in RAG pipelines.

Examples:

• Pinecone
• ChromaDB
• Weaviate

Cloud Platforms (AWS, Azure, GCP): Familiarity with cloud services for deploying and scaling AI applications.

Examples:

• Amazon SageMaker

MLOps (Machine Learning Operations): Understanding practices for deploying and maintaining machine learning models in production, including:

• Version control
• Monitoring

API Development: Knowledge of building and consuming APIs to integrate AI models into existing systems.

Version Control (Git): Proficiency in Git for collaborative development and managing codebases.

Candidates should already have knowledge to complete the following:-

25 Implementation completion checklist

Complete each item with status, owner, evidence link and acceptance date in the project tracker. Empty status means incomplete. This checklist is the release record, not a substitute for the detailed gate tests.

Gate

Required completion item

G0

Hardware, demand, source owners, tier model and licensing approved

G0

Pilot scope and held-out evaluation set approved

G1

Mac baseline, FileVault escrow, reboot and endpoint protection verified

G1

VLAN flows, deny rules, DNS, CA and identity/MFA verified

G2

Pinned native runtime and verified model pass smoke tests

G2

Separate tier data instances, keys and private bindings verified

G2

PostgreSQL, Qdrant and object restore smoke tests completed

G3

Source ACL inheritance, revocation and deletion completed

G3

Chunking/hybrid retrieval, exact identifiers and citations tested

G4

Gateway and broker deny by default; schemas and approval expiry tested

G4

n8n keys, credentials, retries and duplicate-event tests passed

G5

Functional, performance, safety and red-team criteria passed

G5

Full restore drill meets approved RPO and RTO

G5

Pilot owner accepts usefulness and user training

G6

Change window, backup point and rollback manifest approved

G6

Security, data owner and IT Head approve production cohort

G7

Runbooks, on call, asset register and ownership accepted

G7

Access review, patch cadence and capacity review scheduled

• Natural Language Processing
• Graph Query Language
• TypeScript Programming Language
• RESTful API
• Python Programming Language
• C# Programming Language
• Go Programming Language
• Rust Programming Language
• FastAPI Framework
• Flask Framework
• TensorFlow Software
• Transformers
• Scikit-Learn
• Angular
• Express.js
• Software Development
• ASP.NET
• Application Programming Interface (API)
• PHP Programming Language
• Data
• OAuth
• Microservices
• Computing
• Health Insurance
• Information Technology
• General Data Protection Regulation (GDPR)
• Auditing
• ISO Standards
• Access Control
• ChatGPT,
• Prompt Engineering
• RxJS
• Cascading Style Sheets (CSS)
• Material UI
• Webpack
• Relational Database
• Oracle Applications
• MySQL
• MongoDB
• Redis
• Amazon DynamoDB
• Elasticsearch Software

Agentic RAG

Candidates should understand the deployment of the following

Phase 0 — security baseline

• network VLANs;
• FileVault;
• MFA/SSO;
• admin accounts;
• firewall;
• VPN;
• backup;
• secrets vault.

Phase 1 — local inference

Deploy:

Phase 2 — secure RAG

Add:

• Qdrant;
• document scanning;
• access filtering;
• provenance.

Phase 3 — agents

Add read-only agents.

No actions.

Phase 4 — controlled action

Allow:

• drafts;
• CRM proposals;
• task proposals.

Human approval required.

Phase 5 — limited automation

Automate only low-risk deterministic actions.

Phase 6 — monitoring / red-team

Test:

• malicious attachments;
• API abuse;
• credential leak.