Junior AI Engineer

V2 Solutions; Hinoba-an; On-site; PHP 600,000 - 1,200,000; Full time

Job summary

V2 Solutions is seeking a Data Analytics/GenAI Engineer in the Philippines to build LLM-powered apps and RAG pipelines, leveraging embeddings and vector search. You will develop APIs with FastAPI/Flask and deploy to AWS/Azure, ensuring quality and security.

The role emphasizes hands-on GenAI/LLM work plus ML components, observability, and best practices in deployment and governance. Strong Python skills are required.

Qualifications

• Python programming with strong fundamentals and API development.

Responsibilities

• GenAI / LLM Engineering: Build LLM-powered applications (chatbots, copilots, summarization, knowledge assistants) using OpenAI/Azure OpenAI/Anthropic/Gemini or open-source LLMs; implement RAG pipelines with data ingestion, chunking, embeddings, vector search, prompt assembly, response generation; improve response quality via prompt engineering and retrieval tuning.
• ML Engineering (non-platform): Develop and deploy ML components (classification, NLP, forecasting) using scikit-learn / PyTorch / TensorFlow; produce production-grade services with FastAPI/Flask; write clean Python modules and follow best practices.
• Deployment Operations (LLMOps exposure): Support deployment to AWS or Azure; establish observability (logs, latency, token usage); assist with quality, safety, governance (PII redaction, content filtering, prompt-injection mitigation, access controls).

Skills

GenAI / LLM engineering

RAG pipelines

Embeddings

Vector databases

Python programming

API development

Cloud deployment (AWS/Azure)

Git

CI/CD

Containerization

Education

Bachelor of Engineering

Tools

FastAPI

Flask

Docker

Kubernetes

LangChain

LangGraph

LlamaIndex

Semantic Kernel

GitHub Actions

Azure DevOps

Jenkins

Airflow

Databricks

Job description

Educational Requirements

Bachelor of Engineering

Service Line Data Analytics Unit Responsibilities

• GenAI / LLM Engineering: Build LLM-powered applications (chatbots, copilots, summarization, knowledge assistants) using OpenAI/Azure OpenAI/Anthropic/Gemini or open-source LLMs. Implement RAG pipelines: data ingestion, chunking, embeddings, vector search, prompt assembly, response generation. Improve response quality using prompt engineering, retrieval tuning (hybrid search, metadata filters), and basic RAG evaluation practices.
• ML Engineering (non-platform): Develop and deploy ML components (classification, NLP, forecasting) using scikit-learn / PyTorch / TensorFlow as needed. Package AI/LLM solutions into production-grade services using FastAPI/Flask. Write clean, reusable Python modules and follow engineering best practices (testing, logging, code quality).
• Deployment Operations (LLMOps exposure): Support deployment to cloud environments: AWS (SageMaker/ECS/Lambda) or Azure (Azure ML/AKS/App Services). Implement basic observability: logs, error handling, latency tracking, token usage tracking (where applicable). Assist in quality, safety, and governance practices: PII redaction, content filtering, prompt-injection mitigation, secure access controls.

Additional Responsibilities

• Vector databases: Pinecone / Qdrant / Chroma / Weaviate / FAISS.
• Frameworks: LangChain / LangGraph / LlamaIndex / Semantic Kernel.
• Evaluation tools: RAGAS / TruLens / DeepEval, prompt testing frameworks.
• Containerization: Docker (Kubernetes is optional).
• CI/CD exposure: GitHub Actions / Azure DevOps / Jenkins.
• Data pipelines: Airflow / Prefect / Databricks.
• Safety tooling: Presidio, content safety filters, access control patterns.

Technical and Professional Requirements

Python programming (strong fundamentals, OOP, writing APIs, debugging).

Hands-on experience building GenAI/LLM solutions: RAG / embeddings / vector DB / prompt engineering.

Experience with FastAPI or Flask (building and serving APIs).

Understanding of LLM application lifecycle (prompting, evaluation, versioning, deployment basics).

Knowledge of at least one cloud platform: AWS or Azure.

Basic understanding of Git, code reviews, and deployment workflows.

Preferred Skills

Technology->Artificial Intelligence->Artificial Intelligence - ALL Technology->AI-Generative AI->Generative AI - Basic