Description

About the Role

Equinox Engineering is hiring a hands-on AI Developer to help architect and deploy next-generation internal systems that transform how our engineering, business development, and corporate teams operate.

You’ll work directly with our AI Lead to build production-grade, high-leverage tools powered by local LLMs and well structures KGs. Supporting workflows such as intelligent document processing, knowledge graph construction, semantic retrieval augmentation, automated reasoning.

This role is grounded in practical delivery, not research. You will be expected to work with focus and autonomy, leveraging LLMs daily as development partners and taking full ownership of solutions that improve the business.

Key Responsibilities

AI & LLM Engineering

• Build and maintain LLM pipelines for structured extraction, summarization, component layout, and retrieval orchestration
• Write context-driven backend logic using tools like Pydantic, LangChain, LangGraph, or custom orchestration Develop AI pipelines that combine LLM-based reasoning with symbolic system constraints, ensuring outputs remain accurate, explainable, and aligned with structured schemas or business rules (knowledge graph traceable explainability).

Internal Systems Development

Assist in the development of internal tools for:

• Document classification and metadata extraction (PDF, DOCX, Markdown)
• Structured information storage and retrieval via PostgreSQL + pgvector
• Lightweight knowledge graph construction and traversal via Neo4j (multi-graph)
• Semantic and vector-based search with Weaviate or Elasticsearch
• Context-aware internal chat tools powered by hybrid memory pipelines
• Support RBAC-based system access and Azure Entra ID integration
• Contribute to internal UI systems built in React + Tailwind + ShadCN
• Build apps with strong security. Least-privilege access enforcement, Secure token storage and OAuth2 flows, Input validation and output sanitization across APIs and LLM pipelines, Container hardening and environment variable management in Docker, Awareness of common threats (e.g., OWASP Top 10, prompt injection, SSRF, etc.), Use of rate limiting, audit logging, and secure authentication practices

DevOps & Infrastructure

• Manage services using Docker Compose, including multi-container stacks
• Work with Redis for caching, task queues, or session storage
• Maintain clean, version-controlled projects with Git and support internal rollout processes

Technical Must-Haves

Baseline competencies expected on day one:

• Python fluency, especially in AI/data engineering contexts
• Comfortable with async logic, modular design, and typing
• Familiar with FastAPI, Pydantic and SQLAlchemy.

LLM engineering experience

• Have built real systems using OpenAI, Claude, DeepSeek, Mistral, or Ollama models. Can be simple but must show experience.
• Understands context engineering, embeddings, schema extraction, and function calling. Can be simple but must show experience.
• Experience with LangChain or LangGraph is ideal

Context-Driven Development

• You Code using LLMs as collaborators, not just code generators
• Uses tools like Cursor, GPT CLI, Claude CLI, etc. to:
• Scaffold modules
• Summarize and reason through files
• Infer architecture from vague inputs
• Perform rapid iteration using prompt chaining and refinement

Data Stack Experience

• PostgreSQL (including joins, indexing, and basic pgvector usage)
• Bonus: Neo4j (graph schema design and Cypher queries)
• Vector DBs (Weaviate, Chroma, Elasticsearch, etc.)

DevOps Familiarity

• Confident with Docker, especially Docker Compose environments
• Can troubleshoot logs, inter-container networking, and port binding
• Bonus: SSH deployments, basic CI/CD understanding

Git Discipline

• Can fork, branch, PR, and commit in a clean, testable manner

• Avoids breaking shared environments

Skills, Knowledge and Expertise

Mindset & Working Style

We are building quickly. You must bring acceleration.

Context Absorption

• Able to absorb high-level intent and run without constant oversight
• Learns architecture and intent through exposure
• Comfortable working from half-formed notes, diagrams, or conversations. This is a fast-paced engineering environment where you are trusted to build stakeholders visions and communicate what you understand.

Self-Reliant Debugging

• Resolves common issues independently via docs, forums, or logs
• Escalates only when truly blocked or at decision boundaries

Comfort in Ambiguity

• Thrive in non-productized environments and evolving specifications
• Understands that many tools are prototypes that ship, not polished SaaS

Asynchronous Communication

• Provides crisp, regular progress updates without needing reminders
• Flags risks, design changes, and trade-offs clearly

Bonus Points (Nice to Have)

• React + Tailwind experience (Vite + ShadCN UI stack)
• Familiarity with Redis for background queues, task orchestration, or caching
• Domain familiarity in engineering, oil & gas, or industrial project workflows
• Experience with multi-agent LLM workflows or hybrid RAG pipelines

At a minimum, you should be able to demonstrate that you've built and delivered applications, regardless of scale, that integrate modern AI technologies alongside traditional programming practices.

We understand that not every candidate will have experience with every tool or framework listed. If there are areas, you're still developing proficiency in, simply acknowledge them in your application. We're more interested in your ability to learn quickly, reason well, and take ownership than in checking every box.

This is a fast-moving space, and we value builders with curiosity and initiative. If you have a GitHub portfolio, personal projects, or contributions that reflect your experience with AI, LLMs, or backend engineering, and you're excited about working on systems that ship and scale, this role will be a great fit.

About Equinox Engineering Ltd.

Established in 1997, Equinox is a distinguished EPCM service provider globally. Our wide-ranging portfolio includes Sweet and Sour Gas Processing Facilities, Heavy and Conventional Oil Production, Steam Pipeline Systems, and an increasing focus on sustainable energy solutions like Carbon Capture, Utilization, and Storage (CCUS) facilities and pipelines, Renewable Natural Gas (RNG) projects, and Landfill Gas (LFG) initiatives.