Python Developer

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: