Description

Description:

"Knowledge Base & AI Platform Engineering

• Design, develop, maintain and evolve enterprise knowledge-base infrastructure and AI-enabled backend services using

Python and FastAPI.

• Build and optimize ingestion, chunking, embedding, vector indexing, semantic retrieval, reranking, context construction and

grounded RAG pipelines.

• Develop LangChain and/or LangGraph-based agent workflows and reusable agent-runtime capabilities including tool

calling, state management, retries, validation, guardrails and human approval where required.

• Integrate LLMs and enterprise knowledge sources securely through reusable APIs and services; use MCP or similar agent-

tool integration patterns where appropriate.

• Implement PostgreSQL/vector database persistence, metadata filtering and retrieval patterns optimized for accuracy,

latency and scale.

Cloud-Native Delivery & Production Engineering

• Containerize services with Docker and deploy/manage them on Kubernetes.

• Build and maintain CI/CD pipelines using GitHub Actions, Jenkins or equivalent tools, including automated testing and

deployment controls.

• Implement logging, tracing, metrics, dashboards and alerts using OpenTelemetry, Prometheus, Grafana and/or enterprise

monitoring tools.

• Diagnose and resolve issues across APIs, retrieval pipelines, agent execution, databases, containers and distributed

infrastructure.

• Improve performance, reliability, security, scalability and cost efficiency of production AI services.

Quality, Evaluation & Collaboration

• Establish automated tests and evaluation approaches for APIs, retrieval quality, grounded responses and agent behavior.

• Apply secure coding, access controls, audit logging, prompt/output validation and responsible-AI practices.

• Partner with product, architecture, data, security, DevOps and application teams to translate business needs into reusable

knowledge and AI platform capabilities.

• Document architecture decisions, API contracts, operational procedures and knowledge-platform standards.

• Participate in Agile delivery, code reviews, technical design discussions, mentoring and production support"

Skills Required:

"Python & API Engineering

• Strong hands-on experience with Python 3.11+ and production backend development.

• Strong experience building secure, scalable REST APIs and microservices using FastAPI; familiarity with Flask/Django is

useful.

• Strong understanding of asynchronous Python, API design, validation, error handling, authentication/authorization, and

enterprise integration patterns.

Generative AI, RAG & Knowledge Infrastructure

• Hands-on production experience building enterprise Generative AI applications using LLMs, RAG, semantic search,

embeddings, and vector databases; ideally 2+ years of recent GenAI/RAG experience.

• Strong hands-on experience with LangChain and/or LangGraph for agentic workflows, orchestration, tool calling, multi-step

execution, and controlled agent runtimes. LangGraph experience is preferred for stateful agent orchestration.

• Experience designing knowledge ingestion, document chunking, embedding generation, metadata enrichment,

retrieval/reranking, context assembly, grounded generation, and response validation pipelines.

• Experience with PostgreSQL and vector search. pgvector experience is highly desirable but not mandatory.

• Strong understanding of prompt engineering, AI agents, guardrails, human-in-the-loop patterns, AI evaluation, responsible

AI, and secure enterprise data access. Platform Engineering, DevOps & Observability • Strong hands-on experience with Docker and Kubernetes for production AI services. • Experience with Git and CI/CD pipelines using GitHub Actions, Jenkins, or equivalent tools for automated build, test, and deployment. • Experience with production monitoring and observability using OpenTelemetry, Prometheus, Grafana, Splunk/ELK, or equivalent tools. • Ability to troubleshoot performance, reliability, retrieval quality, API, infrastructure, and production issues across distributed AI systems. • Strong analytical, problem-solving, documentation, and cross-functional communication skills. Preferred / Good-to-Have Skills • LangGraph for stateful agent orchestration and reusable agent-runtime patterns. • PostgreSQL pgvector or equivalent production vector-database experience. • Model Context Protocol (MCP), tool integration protocols, or reusable agent-tool interfaces. • OpenTelemetry and production AI/agent observability; GitHub Actions experience. • Experience with automated RAG/agent evaluation, regression datasets, retrieval-quality metrics, and grounded-response"

Keywords:

"Python 3.11, FastAPI, Generative AI, LLM, RAG, Retrieval Augmented Generation, LangChain, LangGraph, AI Agents,

Agentic AI, Prompt Engineering, Semantic Search, Embeddings, Vector Database, PostgreSQL, pgvector, Knowledge Base,

Knowledge Platform, Document Ingestion, Chunking, Retrieval, Reranking, MCP, Model Context Protocol, Tool Calling, Docker, Kubernetes, GitHub Actions, Jenkins, CI/CD, OpenTelemetry, Prometheus, Grafana, AI Evaluation, Guardrails, REST API"

Experience :

5-6 Years

Key Skills

Education

Any Graduate