Senior Artificial Intelligence Engineer

2. The Role

The Senior AI Engineer position is a hands-on, code-first engineering role rather than a management position. In this capacity, you will own features end-to-end, taking projects from initial model integration all the way through to cloud deployment. The role requires working across the full technical stack, involving AI/ML pipelines, backend APIs, frontends, and databases.

3. Experience Requirements

This role requires a minimum of 4 years of hands-on AI/ML development experience (typically 45 years). Experience in Generative AI, LLM systems, and agentic AI development is fully counted toward this requirement. Relevant experience may include any combination of:

• Machine learning and deep learning engineering (model development, training, deployment).
• NLP and applied AI application development.
• Generative AI and LLM application development (RAG, prompt engineering, fine-tuning, embeddings).
• Agentic AI system development (tool use, planning, multi-agent orchestration, agent frameworks).

Note: Pure data analytics, BI/reporting, or infrastructure/support roles without hands-on AI/ML development do not count toward this requirement.

4. Key Responsibilities

4.1 Agentic AI Solutions

• Design, build, and deploy AI/ML pipelines and production-grade agentic workflows.
• Architect multi-agent systems employing orchestration patterns such as router, supervisor, hierarchical, and sequential pipeline models.
• Implement the agentic execution loop reasoning, planning (ReAct), tool/function calling, memory management, and self-correction.
• Develop agents with human-in-the-loop checkpoints, guardrails, permissioned tool access, and auditable action trails.
• Integrate agents with enterprise systems, including relational databases, REST APIs, e-mail, ticketing, and notification platforms.

4.2 Knowledge Graph Engineering

• Design and build knowledge graphs: schema/ontology definition, entity and relationship extraction (LLM-based triple extraction, NER), entity resolution, and graph loading.
• Implement graph pipelines on graph databases such as Neo4j (Cypher), including modelling, ingestion, and query optimization.
• Expose knowledge graphs to AI agents as queryable tools, with schema descriptions and read-only, guardrailed access.
• Build GraphRAG capabilities combining graph traversal with vector retrieval to support multi-hop reasoning and relationship-aware answers.

4.3 Full-Stack Engineering

• Develop full-stack applications with Python backends (FastAPI, Flask/Django) and modern frontends (React, Next.js, or similar frameworks).
• Build RAG systems, LLM integrations, and multi-agent orchestration layers.
• Write and optimize complex queries on enterprise databases.
• Write clean, testable, and well-documented code; participate in code reviews and own production reliability.
• Troubleshoot issues, optimize performance, and ship reliably.

4.4 Data Handling & Data Engineering

• Design and operate data pipelines (batch and streaming) that feed AI systems covering ingestion (Airflow/Dagster), transformation (dbt/Spark), and storage (S3, data warehouses).
• Work across SQL, NoSQL, and graph stores PostgreSQL, MySQL, MongoDB, and Neo4j including schema design, query optimization, and data modelling for AI workloads.
• Implement change-data-capture (CDC), data validation (e.g., Great Expectations), and data-quality controls to ensure trustworthy AI outputs.
• Engineer pipelines for unstructured data, including document/PDF parsing, chunking, and metadata enrichment.

4.5 Data Readiness for AI & Agents

• Build end-to-end RAG pipelines: chunking strategies, embedding generation, vector storage (pgvector, Pinecone, Weaviate, ChromaDB), hybrid retrieval, and reranking.
• Prepare structured data for agent consumption semantic layers, schema documentation, text-to-SQL validation, and entity-relationship/join modelling for safe and accurate agent querying.
• Design context-assembly mechanisms for LLMs prompt construction, context-window management, and long-term agent memory (short-term state plus vector-based and graph-based recall).
• Define data freshness, re-indexing, and evaluation frameworks covering retrieval accuracy and answer faithfulness (e.g., RAGAS, TruLens).

4.6 Cloud, DevOps & Deployment

• Deploy and manage workloads on AWS, Azure, or GCP using containers, serverless architectures, and Infrastructure as Code (IaC).
• Manage containerization and orchestration using Docker and Kubernetes.
• Operate CI/CD pipelines, Git workflows, and DevOps fundamentals in a production setting.

5. Must-Have Qualifications

• Minimum 3 years (typically 34 years) of hands-on AI/ML development experience, inclusive of Generative AI, LLM, and agentic AI work.
• Strong Python proficiency, including FastAPI, Flask/Django, LangChain, LlamaIndex, Hugging Face, and PyTorch/TensorFlow.
• Hands-on experience building production agentic AI systems — tool use, planning, memory, and orchestration (LangGraph/CrewAI).
• Knowledge-graph building experience — schema/ontology design, entity–relationship extraction, entity resolution, and implementation on graph databases (Neo4j/Cypher).
• Full-stack development experience, specifically with APIs and frontends such as React, Next.js, or similar frameworks.
• Enterprise RDBMS expertise (PostgreSQL, MySQL, SQL Server, or Oracle), including schema design, query optimization, stored procedures, and data modelling.
• Familiarity with vector databases such as Pinecone, Weaviate, ChromaDB, or pgvector.
• Working knowledge of data-engineering fundamentals: ETL/ELT pipelines, data quality, and preparation of structured and unstructured data for LLM consumption.
• Experience with cloud platforms, preferably AWS (EC2, Lambda, S3, SageMaker); Azure or GCP are acceptable.
• Knowledge of Docker and Kubernetes for containerization and orchestration.
• Solid grasp of CI/CD pipelines, Git, and DevOps fundamentals.
• Expertise in LLM fine-tuning, embeddings, prompt engineering, and agentic AI concepts — function calling, ReAct, agent state, guardrails, and observability/tracing of agent runs.

6. Good-to-Have Qualifications

• Experience with multi-agent frameworks such as CrewAI, AutoGen, or custom orchestration.
• Agentic RAG experience — agents that self-query, rewrite queries, and iteratively refine retrieval.
• GraphRAG implementations — combining graph traversal with vector retrieval for relationship-aware, multi-hop question answering.
• Familiarity with document databases (MongoDB); RDF/OWL/SPARQL-based ontology engineering is a plus.
• Streaming data pipelines (Kafka/Kinesis) feeding real-time AI agents.
• Previous SaaS or multi-tenant platform experience.
• Knowledge of API gateway design, OAuth2/JWT, and microservices architecture.
• Prior experience working with government or enterprise clients.

Send your updated resume to careers@gaudesolutions.com