Job Title

ABOUT VALIANCEValiance is a deeptech AI company building sovereign and mission-critical solutions for enterprises, public sector, and government institutions. From predictive maintenance and demand planning to sovereign AI for citizen services, we design systems that thrive in high-impact environments. Recognized with the NASSCOM AI GameChangers Award and Aegis Graham Bell Award, and as a Google Cloud Partner, our 150+ engineers and data scientists are shaping the future of industries and societies with responsible AI.THE ROLEWe are looking for an AI Engineer who is not just proficient in using LLMs — but deeply understands how they work, knows when to apply them, and can architect and deploy production-grade Gen AI and Agentic AI systems. You will work on real-world deployments across Document Intelligence and Video Intelligence products, alongside solution delivery for enterprise and public sector clients.This role is structured around a four-stage engineering capability ladder — from mastering LLM fundamentals to building MCP-integrated, tool-driven autonomous agent systems. You are expected to be competent across all four stages.KEY RESPONSIBILITIESLLM Engineering & ReliabilityDesign and optimize prompts using zero-shot, few-shot, chain-of-thought, and role prompting techniquesManage context windows, token budgets, chat memory, and structured/JSON outputs effectivelyImplement validation, retry logic, hallucination handling, and safety guardrails in production pipelinesBuild FastAPI microservices that expose LLM capabilities as reliable, scalable APIsRAG & Retrieval EngineeringArchitect full RAG pipelines — from tokenization and chunking through to output evaluation and traceabilityWork with vector databases, similarity search, and hybrid/ranking retrieval strategiesImplement citation and provenance tracking to make AI responses auditableSelect and tune embedding models appropriate to domain and deployment constraintsAgentic AI SystemsDesign multi-agent architectures with clearly defined roles, responsibilities, and boundariesImplement agent memory strategies — both short-term (in-context) and long-term (persistent)Build tool use and function calling pipelines; delegate and parallelize agent tasks effectivelyDevelop and deploy agentic applications using LangGraph and related frameworksMCP & Tool IntegrationArchitect systems using the Model Context Protocol (MCP) — servers, clients, tools, and resourcesEnable RAG through MCP resources; design and implement multi-step prompt workflow templatesExpose, integrate, and test tools for LLM-driven applications in agentic pipelinesBuild, test, and debug production-grade agentic applications using MCP end-to-endWHAT YOU BRINGTechnical Skills4–6 years of hands-on software engineering experience; 2+ year in applied AI/LLM engineeringStrong Python proficiency — you write clean, tested, production-quality codeWorking knowledge of the OpenAI APIs, LangChain/LangGraph, and at least one vector database (Pinecone, Weaviate, PGVector)Experience building and deploying REST APIs using FastAPI or equivalentFamiliarity with Google Cloud Platform services (Vertex AI, Cloud Run, BigQuery preferred)Understanding of AI evaluation frameworks — you know how to measure and improve model output qualityMindset & BehavioursYou are deeply curious — you don't just use models, you understand what's happening inside themYou take ownership end-to-end — from design to deployment to monitoring in productionYou can translate ambiguous client problems into structured AI solutionsYou communicate clearly with both technical peers and non-technical stakeholdersYou are comfortable with ambiguity and thrive in a high-paced, mission-driven environment