Experience
5–6 Years
Role Overview
We are seeking an experienced engineer to design, develop, maintain, and evolve enterprise knowledge-base infrastructure and AI-enabled backend services using Python and FastAPI. The role focuses on Generative AI, RAG, knowledge infrastructure, agentic workflows, cloud-native delivery, production engineering, quality, evaluation, and secure enterprise AI services.
Roles & Responsibilities
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/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.
- 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 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.
Technical Skills
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 experience building enterprise Generative AI applications using LLMs, RAG, semantic search, embeddings, and vector databases.
- Strong experience with LangChain and LangGraph for agentic workflows, stateful orchestration, tool calling, multi-step execution, and controlled agent runtimes.
- 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.
- 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, GitHub Actions/Jenkins, and CI/CD pipelines 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.
Collaboration & Behavioral Skills
- Strong ownership and stakeholder management.
- Ability to work across Product, Architecture, AI/ML, Data, Security, and DevOps teams.
- Clear technical communication.
- Mentoring and code-review capability.
- Analytical problem solving.
- Agile delivery.
- Focus on reliability, security, scalability, and maintainability.
- Strong documentation and cross-functional communication skills.
Key Skills / Keywords
Python 3.11, FastAPI, LangChain, LangGraph, Generative AI, LLM, RAG, Retrieval Augmented Generation, AI Agents, Agentic AI, Prompt Engineering, Semantic Search, Embeddings, Vector Database, pgvector, PostgreSQL, Knowledge Base, Knowledge Platform, Document Ingestion, Chunking, Retrieval, Reranking, Docker, Kubernetes, Git, GitHub Actions, Jenkins, CI/CD, OpenTelemetry, Prometheus, Grafana, Splunk/ELK, AI Evaluation, Guardrails, REST API.
Skill Category
Digital: Python
