Job Title: Lead Engineer – Neuro Symbolic AI & Agentic Systems
Location: Remote (India)
Employment Type: Full-time
Must Have skills:- Cypher Query, Neo 4J, Neuro Symbolic AI, LangGraph, LangChain, Knowledge Graph
Job Summary
We are seeking a Lead Engineer – Neuro‑Symbolic AI & Agentic Systems to design, develop, and deploy enterprise‑scale intelligent systems that fuse Generative AI, autonomous agents, symbolic reasoning, and Knowledge Graphs.
The ideal candidate will bring strong expertise in Python, LLM‑based systems, agentic frameworks, and knowledge‑centric AI, with hands‑on experience delivering production‑grade GenAI or agentic solutions grounded using Knowledge Graphs (Neo4j).
you will lead the architecture and implementation of agentic, reasoning‑driven AI platforms, mentor engineers, shape technical strategy, and enable scalable AI solutions across multiple enterprise domains.
Key Responsibilities
- Lead the design and implementation of agentic AI systems using frameworks such as LangGraph, AutoGen, LangChain, or similar.
- Architect neuro‑symbolic AI solutions that integrate:
- Large Language Models (LLMs)
- Symbolic reasoning, rules, and constraints
- Knowledge Graphs for grounding and explainability
- Design and implement Knowledge Graphs using Neo4j for:
- LLM grounding and hallucination mitigation
- Agent memory, planning, and reasoning
- Explainable multi‑hop inference
- Develop and optimize Cypher queries, graph schemas, and indexing strategies in Neo4j.
- Implement Graph‑RAG and hybrid retrieval pipelines combining vector databases and Knowledge Graphs.
- Architect and deploy scalable APIs (REST/WebSocket) for AI and agent workflows.
- Deploy and maintain multiple GenAI / Agentic AI solutions in production, ensuring reliability, scalability, and security.
- Integrate SQL, No‑SQL, vector, and graph databases (Postgres, MongoDB, Neo4j, ChromaDB, etc.).
- Provide technical leadership and mentorship to AI and platform engineers.
- Collaborate with cross‑functional teams to deliver AI solutions across banking, insurance, and healthcare domains.
- Ensure governance, compliance, observability, and robustness of AI systems.
- Stay current with advancements in Generative AI, agentic systems, symbolic reasoning, and knowledge‑centric AI.
- Document system designs and present solutions to both technical and non‑technical stakeholders.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or related field.
- 7+ years of overall professional experience in AI/ML, Data Science, or advanced software engineering.
- 5+ years of strong hands‑on experience in Python, with solid software engineering best practices.
- 3+ years of experience building Generative AI or Agentic AI systems, including production deployments.
- Hands‑on experience with LLMs, prompt engineering, and model integration.
- Practical experience with Knowledge Graph design and implementation.
- Strong experience working with:
- Neo4j and Cypher
- SQL, No‑SQL, vector databases
- Proven experience deploying production‑grade AI systems with scalability and reliability considerations.
- Solid understanding of data pipelines, ETL, and data modeling.
Preferred Qualifications
- Experience with LangGraph, AutoGen, LangChain, or similar agent orchestration frameworks.
- Experience designing multi‑agent systems and long‑horizon reasoning workflows.
- Knowledge of neuro‑symbolic AI concepts, logic‑based reasoning, or rule‑based systems.
- Experience with Graph Data Science (GDS) or graph‑based inference techniques.
- Familiarity with MLOps practices (CI/CD, monitoring, experimentation, retraining).
- Experience working in regulated or enterprise environments.
- Contributions to open‑source projects, internal AI platforms, or applied AI research.
- Strong problem‑solving skills and ability to thrive in fast‑paced R&D environments.
