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Salary
$29k – $121k per year (Estimated)
Location
In office (Bengaluru)
Overview
Company
Impact
Profile match
UnionSys caters to software solutions for accelerating businesses using full-cycle software development with branches worldwide from North America, Middle-East to South East Asia.

Responsibilities:

  • Design, build, and enhance AI and generative AI solutions using Python.
  • Develop and manage agentic workflows for complex task execution.
  • Create effective prompts, system instructions, and agent interaction patterns.
  • Build AI applications using Python-based tools, frameworks, and ADKs.
  • Work with MCP, agent/tool integration patterns, and orchestration frameworks.
  • Implement and manage RAG-based techniques for context-aware AI use cases.
  • Use MLflow or similar tools to support experiment tracking and model lifecycle workflows.
  • Collaborate in an Agile delivery environment with cross-functional teams.
  • Translate business requirements into scalable AI-enabled solutions.
  • Support testing, debugging, and optimisation of Python-based AI applications.

Requirements:

  • Strong hands-on experience in Python development.
  • Strong understanding of AI and generative AI concepts.
  • Experience building agentic AI solutions.
  • Hands-on experience with Python ADK or similar agent development frameworks.
  • Good understanding of prompt engineering.
  • Ability to write effective instructions for agents and define agent behaviours.
  • Experience with agentic flows / orchestration patterns.
  • Working knowledge of MCP (Model Context Protocol).
  • Experience with RAG techniques.
  • Familiarity with MLflow or similar ML lifecycle tooling.
  • Exposure to LangGraph or graph-based orchestration frameworks.
  • Good understanding of Agile / Agile team ways of working.
  • Strong debugging, problem-solving, and Python coding skills.

Good to have:

  • Experience in big data ecosystems.
  • Hands-on experience with PySpark.
  • A complementary secondary skill aligned to the primary AI/Python skillset, such as data engineering, cloud, or MLOps.
  • Exposure to scalable data processing pipelines.
  • Familiarity with deploying AI workloads in enterprise environments.
  • Experience in Python (strong proficiency), LangChain and LangGraph, RAG (Retrieval-Augmented Generation), multi-agent systems, and memory systems (long-term/short-term, persistence, and conversational checkpoints).
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