Full Stack Developer - AI platform

An AI-powered maritime intelligence platform requires a freelance full stack developer to establish a production-grade Pinecone vector database and supporting backend for knowledge retrieval. The role focuses on designing and implementing a robust retrieval stack (RAG) that ingests maritime data, stores high-quality embeddings with rich metadata, and exposes secure, scalable APIs for downstream applications. The solution should integrate with PostgreSQL as a system of record, support efficient updates, and deliver low-latency semantic and hybrid search. The ideal candidate combines data engineering pragmatism with product-minded execution, ensuring documentation, observability, and maintainability are first-class. Work is fully remote, with scope and milestones defined collaboratively.

Deliverables

• Design and provision a Pinecone index (pods/collections) with appropriate dimensionality, metric, and replication/sharding strategy for target latency and scale.
• Metadata schema and namespaces enabling faceted filtering (e.g., vessel type, geography, timestamp, source) and multi-tenant separation where needed.
• Embedding pipeline: chunking strategy, text normalization, deduplication, PII handling, and embedding generation (OpenAI/Cohere/Voyage or equivalent), with retries and backoff.
• Ingestion & sync service to upsert vectors from PostgreSQL and external maritime sources (e.g., AIS feeds, vessel registries, reports), including change-data-capture or periodic batch jobs.
• Minimal reference app (e.g., Next.js/Node or Python/FastAPI) demonstrating query, filter, and retrieval flows, including API keys and role-based access.

Requirements

• Demonstrable experience building vector-based retrieval systems using Pinecone (index design, metadata filtering, upserts, query optimization).
• Strong proficiency with PostgreSQL (schema design, indexing, ETL/ELT) and integrating it as the source of truth for embedding pipelines.
• Familiarity with LLM/RAG patterns: embedding model selection, chunking heuristics, reranking, caching, and evaluation of retrieval quality.
• Cloud fluency (AWS/GCP/Azure), containerization (Docker), CI/CD, and infrastructure-as-code.
• Data engineering skills: streaming/batch ingestion, file formats (JSONL/Parquet), and job orchestration (e.g., Airflow, Temporal, or serverless schedulers).
• Security best practices for secrets, token management, and access control; comfort with monitoring/observability tooling.
• Excellent communication, proactive status updates, and ability to work asynchronously in a remote setup.

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