About the Role

Our client is looking for an AI Automation Engineer to design, build, and operate AI-powered automations that reduce manual effort across business and engineering workflows.

This is a hands-on engineering role focused on converting ambiguous, human-in-the-loop processes into reliable, monitored, production-grade automations using LLMs, agentic frameworks, APIs, and workflow orchestration tools .

Minimum Qualifications

- 3–5 years of professional experience in Software Engineering, Data Engineering, or Automation Engineering , including at least 1 year of hands-on LLM/AI experience.
- Solid Python development skills, including API integration, asynchronous programming, testing, packaging, and maintainable code .
- Practical experience using LLM APIs such as OpenAI, Anthropic Claude, Gemini, or Amazon Bedrock in production applications.
- Hands-on experience with at least one agentic AI/orchestration framework , such as LangChain, LangGraph, LlamaIndex, CrewAI, Semantic Kernel, or AutoGen.
- Demonstrated experience building RAG systems using vector stores such as pgvector, Pinecone, Weaviate, Qdrant, Milvus, or OpenSearch.
- Strong understanding of prompt engineering, context design, structured outputs, JSON schemas, tool/function calling, and validation .
- Familiarity with Model Context Protocol (MCP) and building MCP servers to expose internal systems as agent tools.
- Strong experience with REST/GraphQL APIs , OAuth, token-based authentication, webhooks, pagination, and rate-limit handling.
- Working knowledge of SQL and data modeling .
- Experience with Git, code reviews, and CI/CD .




- Cloud experience with AWS, Azure, or GCP , including compute, serverless functions, queues, secrets management, and managed AI services.
- Basic Docker/containerization knowledge.
- Strong stakeholder-management skills, including requirements gathering, prototyping, demonstrations, and expectation management.

Key Responsibilities

- Build and deploy end-to-end AI automations and agentic workflows integrating LLMs with internal systems, APIs, and data sources.
- Design and implement RAG pipelines , including chunking, embeddings, vector search, re-ranking, and grounded response generation.
- Develop tool/function-calling integrations that allow AI agents to interact with ticketing systems, ERP platforms, document stores, and internal APIs.
- Build MCP servers to expose internal systems as tools for AI agents.
- Partner with stakeholders to identify high-value automation opportunities and prioritize an automation roadmap based on ROI and feasibility .
- Develop production-grade Python services and jobs , including API clients, data transformations, schedulers, and event-driven handlers.
- Build LLM evaluation frameworks using golden datasets, regression testing, accuracy checks, hallucination detection,



and quality gates.
- Implement observability and tracing to monitor failures, costs, latency, and quality degradation .
- Design human-in-the-loop checkpoints, approval workflows, and safe fallbacks where full autonomy is not appropriate.
- Optimize AI applications for cost and latency through model selection, caching, batching, prompt optimization, and model routing.
- Document architecture, runbooks, and operational procedures and support automations after production deployment.
- Ensure AI solutions comply with security, privacy, responsible AI, access-control, and PII requirements .

Preferred Qualifications

- Experience with workflow automation platforms such as n8n, Zapier, Power Automate, Airflow, Temporal, Prefect, or Dagster .
- RPA experience with UiPath, Automation Anywhere, or Blue Prism .
- Experience with LLM observability and evaluation tools such as LangSmith, LangFuse, Arize, Braintrust, Ragas, or DeepEval .
- Experience designing multi-agent systems , including planner/executor and supervisor architectures.
- Experience with document intelligence, OCR, PDF parsing, table extraction, and multimodal/vision models .
- Frontend experience with React.js, Next.js, or TypeScript .
- Exposure to Terraform, CloudFormation, or Kubernetes .
- Experience with enterprise AI governance, including audit trails, data residency, AI risk reviews, and access-scoped retrieval .
- Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

📌 AI Automation Engineer (Bangalore Metropolitan Area)
🏢 Applicantz
📍 Bangalore Metropolitan Area