Job Summary

Executive Overview: We are looking for an experienced, hands-on Senior AI RAG Platform Engineer with 5+ years of software engineering and applied AI experience to lead the architecture, configuration, optimisation, and production deployment of our generative AI capabilities. In this role, you will be the technical owner responsible for designing advanced Retrieval-Augmented Generation (RAG) pipelines, managing vector indexing and retrieval lifecycle, orchestrating foundation models (both open-source and proprietary), and embedding these AI services natively into our in-house custom-developed platform.

Responsibilities

- Advanced RAG Architecture Pipeline Configuration - End-to-End Retrieval Pipelines: Design, configure, and optimise enterprise RAG pipelines combining dense vector search, sparse keyword search (BM25), reciprocal rank fusion (RRF), and cross-encoder reranking models (Cohere Rerank, BGE).
- Document Parsing ETL: Build robust ingestion workflows for parsing, segmenting, and embedding multi-format documents (PDFs, Markdown, relational data, APIs) with semantic chunking and hierarchy preservation.
- Vector Lifecycle Operations: Configure and maintain production vector databases (e.g., pgvector, Qdrant, Pinecone, Milvus, Weaviate), including multi-tenant partitioning, payload indexing, and HNSW index tuning.
- AI Model Selection, Orchestration Agents - Model Integration: Evaluate, select, and configure foundation models (OpenAI GPT-4o, Anthropic Claude 3.5, Llama 3.1/3.3, Mistral) based on cost, context limits, latency, and task complexity.
- AI Model Selection, Orchestration Agents - Agentic Workflows Tool Calling: Implement structured schema decoding (JSON/Pydantic), function calling, and multi-step agentic execution flows (using LangGraph, LlamaIndex, or custom workflows).
- AI Model Selection, Orchestration Agents - Fine-Tuning Serving (Optional/Targeted): Conduct parameter-efficient fine-tuning (PEFT / LoRA / QLoRA) when necessary, and deploy open-weight models via inference servers such as vLLM, TensorRT-LLM, or Ollama.




- Custom Platform Integration Engineering - API Microservice Development: Design and expose scalable REST, gRPC, and WebSocket streaming endpoints using Python (FastAPI, asyncio) to interface between LLM workflows and our proprietary platform microservices.
- Custom Platform Integration Engineering - State Memory Management: Build sliding-window conversational memory, persistent session tracking, semantic caching (Redis / GPTCache), and tenant-level access control at the retrieval boundary.
- Custom Platform Integration Engineering - Async Event-Driven Processing: Integrate heavy inference and document embedding pipelines into distributed message brokers and queues (Celery, Kafka, RabbitMQ, SQS).
- LLMOps, Guardrails Quality Assurance - Quantitative Evaluation: Establish continuous RAG evaluation frameworks (Ragas, TruLens, DeepEval) tracking Faithfulness, Answer Relevance, Context Precision, and Hallucination rates.
- LLMOps, Guardrails Quality Assurance - Guardrails Security: Implement strict guardrails (NeMo Guardrails, Llama Guard), prompt-injection mitigation, PII masking, and data privacy governance.
- LLMOps, Guardrails Quality Assurance - Observability: Track latency, token utilization, rate limits, and end-to-end trace flows using Langfuse, Arize Phoenix, LangSmith, or OpenTelemetry.

Qualifications

- Must-Have Requirements: 5+ years of qualified backend/software engineering experience, with primary expertise in Python (FastAPI, Pydantic, asyncio, multiprocessing).
- Must-Have Requirements: 2+ years of hands-on experience designing and operating RAG pipelines and production LLM-based solutions.
- Must-Have Requirements:



Deep practical knowledge of AI orchestration frameworks: LlamaIndex, LangChain, LangGraph, or custom agent engines.
- Must-Have Requirements: Production experience with vector databases (e.g., pgvector, Qdrant, Pinecone, Weaviate, Milvus).
- Must-Have Requirements: Strong background in microservices architecture, RESTful/gRPC design, WebSocket streaming for token generation, and distributed caching (Redis).
- Must-Have Requirements: Experience with relational databases (PostgreSQL) and query performance tuning.
- Must-Have Requirements: Proficient in Docker, container orchestration (Kubernetes), CI/CD, and major cloud ecosystems (AWS, GCP, or Azure).
- Must-Have Requirements: Familiarity with RAG evaluation metrics and debugging hallucination/grounding issues.
- Preferred / Nice-to-Have: Experience deploying high-concurrency LLM inference backends using vLLM or TensorRT-LLM.
- Preferred / Nice-to-Have: Experience with Row-Level Security (RLS) and Role-Based Access Control (RBAC) applied to vector knowledge bases.
- Preferred / Nice-to-Have: Background in embedding model domain adaptation or LoRA fine-tuning.
- Preferred / Nice-to-Have: Prior experience working within custom SaaS or internal enterprise platforms.

Target Technical Stack

- Domain Technologies : Python 3.11+, FastAPI, Pydantic, asyncio, Node.js (secondary)
- RAG Orchestration : LlamaIndex, LangChain, LangGraph, DSPy, Unstructured.io
- Vector DBs / Search : pgvector (PostgreSQL), Qdrant, Pinecone, Weaviate, BM25 / OpenSearch
- LLMs Embeddings : OpenAI, Anthropic Claude, Llama 3.1/3.3, Mistral, BGE / Voyage Embeddings
- LLMOps Evaluation : Ragas, TruLens, Langfuse, Arize Phoenix, NeMo Guardrails
- Infrastructure : Docker, Kubernetes, Redis, RabbitMQ/Kafka, AWS/GCP, Terraform

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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