ROLE_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/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"
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 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. Strong analytical, problem-solving, documentation and cross-functional communication skills."
DESIRABLE_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."
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, GitHub Actions, Jenkins, CI/CD,
OpenTelemetry, Prometheus, Grafana, AI Evaluation, Guardrails, REST API"
EXPERIENCE_RANGE_IN_REQUIRED_SKILLS
5-6 Years
Role Descriptions: 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.
Essential 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.
Desirable Skills:
Keyword:
Skills: Digital : Python
Experience Required:
or continue with email
"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"
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 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. Strong analytical, problem-solving, documentation and cross-functional communication skills."
DESIRABLE_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."
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, GitHub Actions, Jenkins, CI/CD,
OpenTelemetry, Prometheus, Grafana, AI Evaluation, Guardrails, REST API"
EXPERIENCE_RANGE_IN_REQUIRED_SKILLS
5-6 Years
Role Descriptions: 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.
Essential 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.
Desirable Skills:
Keyword:
Skills: Digital : Python
Experience Required:
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