Revalsys Technologies - Senior AI/ML Full Stack Developer

Description : 3+ : Type : Full-Time

Role Overview

We are seeking a versatile Senior AI/ML Full-Stack Developer to lead the design, development, and integration of Generative AI solutions.

This role is ideal for a developer who bridges the gap between robust backend engineering and cutting-edge AI implementation.

You will be responsible for taking concepts from initial PoCs to production-ready, scalable RAG (Retrieval-Augmented Generation) applications.

Key Responsibilities

• End-to-End AI Engineering : Architect and build full-stack applications that leverage Large Language Models (LLMs) to solve complex business problems.
• RAG Architecture : Design and optimize Retrieval-Augmented Generation pipelines, ensuring high-quality context retrieval through advanced embedding techniques and vector databases.
• Rapid Prototyping : Drive the innovation cycle by building high-fidelity Proof of Concepts (PoCs) for new AI-powered features.
• API & Backend Systems : Develop high-performance APIs and microservices (using Python/FastAPI or Node.js) to manage AI inference workflows and data processing.
• LLM Orchestration : Integrate and fine-tune interactions with various LLM providers (OpenAI, Anthropic, or Open Source via HuggingFace) using orchestration frameworks.

Technical Requirements

AI & LLM Specialization :

Frameworks : Proficiency in LangChain, LlamaIndex, or Haystack.

• Vector Databases : Hands-on experience with Pinecone, Milvus, Weaviate, or ChromaDB.
• Models : Deep understanding of prompting, context window management, and model selection across OpenAI, Claude, and Llama 3.
• Concepts : Solid grasp of semantic search, tokenization, and embedding models.

Full-Stack Engineering

Backend :

3+ years of experience in Python (FastAPI/Flask) or Node.js. Knowledge of .NET is a strong plus.

• Frontend : Expertise in React.js or Next.js for building responsive, AI-driven user interfaces.
• Cloud & DevOps : Familiarity with Docker and deployment on cloud platforms (AWS, Azure, or GCP).
• Databases : Experience with both SQL (PostgreSQL) and NoSQL environments.

Preferred Qualifications

• Proven track record of building and deploying AI side projects or commercial PoCs.
• Experience with Agentic Workflows (e.g., CrewAI or AutoGen).
• Strong understanding of AI evaluation metrics and monitoring.

(ref:hirist.tech)