Aurigo - Senior AI Developer - RAG/Python

Role Overview :

We are seeking a Senior AI Developer (GenAI specialization) to design, build, and operate production-grade Generative AI systems that enable natural-language interaction over large-scale enterprise document ecosystems.

This is a builder and systems-engineering role, not a research or analytics position. You will work from first principles to engineer robust, scalable, and observable GenAI platforms, owning critical components across the lifecycle from document ingestion and retrieval to LLM orchestration, API serving, and cloud deployment.

You will collaborate closely with senior engineers and architects while taking clear ownership of execution-level design and delivery for core GenAI systems.

Key Responsibilities :

GenAI Systems & Application Development :

- Design and build enterprise-grade GenAI applications (chatbots, copilots, assistants) that support natural-language search across large document repositories and structured data.

- Develop end-to-end RAG pipelines, including document ingestion, intelligent chunking, metadata extraction, indexing, retrieval, and response generation.

- Implement agentic and tool-using AI workflows for complex reasoning, orchestration, and large-scale document interaction.

Retrieval & Knowledge Engineering :

- Build and optimize vector database pipelines for semantic search, context management, chat memory, and source attribution.

Implement advanced retrieval strategies, including :

1. Hybrid search (semantic + keyword)

2. Multi-stage retrieval and re-ranking

3. Relevance scoring and evaluation techniques

- Debug and improve retrieval quality, grounding accuracy, and hallucination mitigation in production systems.

LLM Integration & Optimization :

- Integrate and optimize LLMs via AWS Bedrock or Azure OpenAI, including:

1. Context window and token optimization

2. Streaming responses

3. Citation and traceability mechanisms

- Apply LLM optimization techniques (prompt design, fine-tuning where applicable, and model compression) to balance response quality, latency, and cost.

Backend, APIs & Cloud Deployment :

- Build production-ready REST APIs using FastAPI (or similar frameworks), with proper error handling, authentication, and concurrency support.

- Deploy and scale GenAI services on AWS, handling high-throughput, concurrent user traffic.

- Identify and resolve performance bottlenecks, latency issues, and infrastructure cost inefficiencies.

Quality, Monitoring & Governance :

- Implement evaluation metrics and monitoring for GenAI/RAG systems (retrieval quality, latency, failure modes).

- Apply best practices around AI safety, ethics, governance, and observability in production environments.

- Contribute to internal documentation, reusable components, and GenAI engineering standards.

- Support mentoring and knowledge-sharing to help evolve the organizations GenAI engineering culture.

Required Skills & Experience :

Core Technical Skills (Must-Have) :

- Strong hands-on experience building GenAI / LLM applications from scratch, beyond simple API consumption or demos.

- Deep practical expertise in:

1. Document chunking strategies

2. Metadata extraction

3. Multi-format document pipelines (PDF, DOC, HTML, etc.)

4. Context and memory management for conversational systems

5. Vector databases in production: indexing, retrieval optimization, and performance tuning.

6. Embeddings and semantic search: sentence transformers, similarity search, distance metrics.

7. Advanced RAG techniques: hybrid retrieval, re-ranking, and multi-step retrieval.

- Backend engineering experience with RESTful APIs (FastAPI or equivalent).

- Cloud-native development and deployment on AWS.

LLM & Platform Skills :

- Production LLM integration using AWS Bedrock, Azure OpenAI, or similar platforms.

- Token efficiency, streaming responses, and response grounding.

- Experience with evaluation frameworks for RAG systems and conversational AI.

- Solid understanding of monitoring, reliability, and cost optimization for AI systems.

- Candidates are expected to have deep hands-on ownership in core GenAI systems, with strong working exposure across adjacent areas such as agentic workflows, evaluation, and optimization.

Good to Have :

- Experience with agentic frameworks and tool orchestration.

- Exposure to model fine-tuning, distillation, or compression techniques.

- Familiarity with AI observability tools and governance frameworks.

- Experience supporting enterprise security, compliance, and data privacy requirements.

Why Join Us :

- Build real, production-grade GenAI systems used at enterprise scale.

- High ownership with deep technical impact.

- Opportunity to help shape GenAI engineering standards and best practices.

- Work at the intersection of AI, backend systems, and cloud engineering in a product-driven environment

(ref:hirist.tech)