About Gaude AI Solutions

Gaude Business and Infrastructure Solutions Pvt. Ltd. (Gaude AI Solutions) is a 4-year-old AI and enterprise software company based at Technopark in Trivandrum. The company serves a diverse range of clients across India, the US, the UK, the UAE, and Saudi Arabia. Rather than focusing on simple proofs of concept (POCs), Gaude is dedicated to building and scaling production-grade AI products and platforms.

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.

Experience Requirements

This role requires a minimum of 4 years of hands-on AI/ML development experience (typically 4–5 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.

Key Responsibilities

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.

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.

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.

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.

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).

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.

Must-Have Qualifications

- Minimum 4 years (typically 4–5 years) of hands-on AI/ML development experience,



inclusive of Generative AI, LLM, and agentic AI work.
- Robust 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.

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.

What We Value

- A focus on shipping working software rather than creating slide decks.
- An ownership mindset where you see a problem and fix it.
- A commitment to staying current with AI tooling and trends.
- The ability to communicate clearly with both technical and non-technical stakeholders.

Details

- Location: Technopark, Trivandrum (on-site preferred; hybrid available for exceptional candidates).
- Type: Full-time.
- No. of Openings: 3
- Send your updated resume to [email protected]

📌 Senior AI Engineer (Thiruvananthapuram)
🏢 Gaude Business u0026 Infrastructure Solutions
📍 Thiruvananthapuram