The Opportunity

We are building a platform that merges real-time physics, structured knowledge, and autonomous intelligence. We are looking for developers ready to tackle one (or more) of those challenges:

• Ingestion: Handling massive streams of sensor data with minimal latency.
• Intelligence: Making LLMs deterministic and reliable within robust multi-agent systems.
• Context: Modeling complex ontologies to map thousands of physical assets.

You will join a high-performance team, contributing to designing and delivering Advanced Agentic AI Systems across the full stack — from data pipelines and knowledge graphs, through agent orchestration, to the interactive UI engineers and analysts use every day.

Core Responsibilities

GenAI & Autonomous Agents

• Build Autonomous Agents: Deploy stateful agents (using LangGraph) that plan tasks, query Knowledge Graphs, and execute tools without hallucinating.
• Advanced RAG: Build Graph-RAG pipelines that combine semantic search with structured knowledge traversal for grounded answers.
• Build agents capable of integrating with engineering tools, simulators, databases, and knowledge sources.
• Collaborate with domain experts to align agent behavior with technical expectations and constraints.
• Implement safeguards to ensure accuracy, traceability, and reliability of AI-generated outputs.
• Continuously optimize prompting, agent orchestration, and performance under real-world conditions.
• High-Performance APIs: Build low-latency Python services (FastAPI) to serve live data to frontend and AI models.
• Rapid Delivery: Adopt a “deliver fast” mentality without compromising code quality, testing, or API design standards.

Full-Stack & Visualization

• Build interactive React-based AI chat assistant including canvas/dashboard views

Technology Stack & Desired Profile

You do not need to be an expert in every single tool, but you must be able to discuss technical trade-offs across the entire stack:

• GenAI: LangGraph, LangChain, LiteLLM, Azure OpenAI/Anthropic/Local SLMs, MCP (Model Context Protocol).
• Backend: Python 3.12+ (FastAPI), Kafka, Neo4j.
• Frontend: React, Angular 16+, TypeScript, RxJS, Plotly.js.
• Data: Apache Airflow, Trino, Apache Iceberg, MinIO, Delta Lake, Polars/Spark.
• Infrastructure: Kubernetes (EKS/AKS/K3s/on-prem), Terraform, Hybrid Cloud.

Desired Profile

• Problem Solver: You dig into logs to find the root cause of a data spike, a silent failure, or a disconnected node in a graph.
• Modern Pythonista: You are up-to-date with modern Python async patterns and typing, ensuring code is high-performance and maintainable.
• You understand Big O notation and how to optimize code for CPU/memory efficiency.
• Full-Stack Mindset: You move comfortably between backend services, agent orchestration, and frontend UI work, and can reason about trade-offs across the whole stack.

Qualifications

Must Haves:

• GenAI: Practical experience building applications with LLMs, Agentic Frameworks (LangGraph), and Vector Databases.
• 5+ years of experience in Python Backend development.
• 2+ years of experience with modern frontend frameworks (React and/or Angular) and TypeScript.
• Advanced English communication skills.
• Computer Science fundamentals: Data Structures and Algorithms.
• Strong experience building and documenting REST APIs (FastAPI).

Good to Have:

• Agent Evaluation: Experience building eval pipelines for agentic systems (e.g., Lang Smith, Ragas, or custom harnesses) to test task success, groundedness, and hallucination rate.
• Streaming: Proficiency with Streaming Technologies (Kafka, Flink, Spark) and Time-Series Databases (TimescaleDB, InfluxDB).
• Graph: Hands-on experience with Graph Databases (Neo4j/Cypher), SQL database design, and Hybrid Search strategies.
• Background in Heavy Industry, O&G, or IoT data (MQTT, OPC UA).
• Experience with Local LLMs (Ollama) for privacy-focused deployments.
• Familiarity with Data Lineage or Metadata management.