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Python AI Engineer

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Onsite

Python AI Engineer

Experis

Python AI Engineer

Skills

DjangoFlaskPythonGitDatadogData AnalysisAIElasticsearchFastAPIComplianceAzure

2 months ago

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Important information


Contract type:

Permanent

Location:

Pune, Maharashtra, India

Work mode:

Onsite

Published on:

19 August 2026

What they need


Position: Python AI Engineer

Job Type: Contract To Hire(C2H)
Experience: 34 Years
Location: Pune
Interview Mode: Unberry Assessment, Technical Interview (F2F)

Role Summary

We’re looking for an engineer who can build production backend services and design/operate Retrieval-Augmented Generation (RAG) and agentic AI systems on Microsoft Azure. This is a hands-on role spanning API development, cloud data pipelines, and LLM-powered conversational agents—not a pure research or pure DevOps role.

Key Responsibilities

  • Design, build, and maintain backend services using Python/FastAPI or equivalent.
  • Integrate backend services with multiple upstream AI/chat systems via REST APIs.
  • Build and tune retrieval pipelines on Azure AI Search, including indexing strategies, chunking, embeddings, hybrid/semantic search, and relevance evaluation.
  • Design, configure, and evaluate AI agents using Microsoft Azure AI Foundry Agent Service or equivalent LLM agent/orchestration frameworks.
  • Ground AI agents in enterprise knowledge sources with citations.
  • Set up and manage Azure infrastructure, including Blob Storage data pipelines, API Management as an AI/API gateway, Managed Identities, RBAC, and Log Analytics for usage and cost monitoring.
  • Evaluate new platform capabilities by distinguishing GA and preview features and assessing production readiness, support, and SLA implications.
  • Communicate technical trade-offs clearly to stakeholders.
  • Own end-to-end quality and test retrieval accuracy using realistic query patterns, including multi-part and comparison-style questions across large content catalogs.
  • Write clear technical documentation covering architecture decisions and integration contracts for technical and non-technical stakeholders.

Required Skills & Experience

  • Strong Python backend development experience.
  • Experience with REST API design, async programming, and frameworks such as FastAPI or similar.
  • Experience with Flask/Django is acceptable when combined with async/API design fundamentals.
  • Hands-on experience with OAuth2/token-based authentication flows.
  • Experience with API key/secret management and encryption of data in transit, such as AES-GCM or equivalent.
  • Practical experience with Azure AI Search or comparable vector/hybrid search products such as Elasticsearch, Pinecone, or Weaviate.
  • Experience with index design, indexers/data pipelines, embeddings, and relevance tuning.
  • Experience building or integrating LLM-powered applications.
  • Strong understanding of RAG pipelines, prompt/instruction design, function/tool calling, and citation/grounding patterns.
  • Familiarity with cloud-native service patterns, including Managed Identities, RBAC, API gateways, and centralized logging/observability.
  • Experience with Azure Monitor/Log Analytics or equivalent tools such as CloudWatch or Datadog.
  • Ability to read vendor documentation and release notes and independently assess cloud AI capabilities for production use.
  • Git-based version control and collaborative code review experience.

Preferred / Nice-to-Have

  • Direct experience with Microsoft Azure AI Foundry Agent Service.
  • Experience with Foundry IQ, Azure AI Search agentic retrieval, or Microsoft Copilot Studio.
  • Experience designing chatbots/conversational agents supporting factual Q&A and multi-entity comparison-style questions over large product/document catalogs.
  • Experience with Azure API Management or another API gateway for usage metering, rate limiting, and per-client governance.
  • Background in regulated or compliance-sensitive industries such as financial services, healthcare, or insurance.
  • Experience with data access boundaries and auditability.
  • Exposure to markdown-based or structured-content knowledge bases as RAG data sources.

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