AI Automation Engineer

Quarks Technosoft Pvt. Ltd.; Bengaluru; Hybrid; INR 1,200,000 - 2,100,000; Full time

Job summary

Quarks Technosoft Pvt. Ltd. is seeking an AI Automation Engineer to design, build, and operate AI-powered automations that reduce manual effort in workflows.

You will turn ambiguous processes into reliable, production-grade automations using LLMs, agentic frameworks, APIs, and orchestration tools. You will collaborate with data engineers and platform teams to prototype, validate, and harden automation services with strong logging, tests, and observability.

Qualifications

• 3–5 years in software, data, or automation engineering.
• Experience with LLMs or AI services in real apps.
• Strong Python skills, async programming, API integration.
• Hands-on with an agent/orchestration framework.
• Experience building RAG systems and vector stores.
• Knowledge of MCP servers and tool integration.

Responsibilities

• Build end-to-end AI automations integrating LLMs with internal systems.
• Design RAG pipelines: chunking, embeddings, vector search, grounding.
• Develop tool and function-calling integrations to expose internal systems as agent tools.
• Collaborate with stakeholders to map processes and prioritize automation ROI.
• Write production Python services with logging, retries, and error handling.
• Create evaluation harnesses for prompts, models, and outputs.
• Instrument observability to detect failures, latency, and cost spikes.
• Implement human-in-the-loop and safe fallbacks where needed.
• Optimize cost and latency via model selection and routing.
• Document architecture, runbooks, and handover material.
• Adhere to security, privacy, and responsible-AI guidelines.

Skills

Python

LLMs

AI automation

API integration

LangChain

RAG pipelines

REST/GraphQL

CI/CD

Data engineering

Education

Bachelor's degree in CS/Engineering

Tools

LangSmith

Pinecone

Qdrant

LlamaIndex

Docker

Kubernetes

Job description

Primary Skills - Java Full Stack and AI Automation

Role Summary

We are looking for an AI Automation Engineer to design, build, and operate AI-powered automations that remove manual effort from business and engineering workflows. This is a hands-on build role: you will take ambiguous, human-in-the-loop processes and turn them into reliable, monitored, production-grade automations using LLMs, agentic frameworks, APIs, and workflow orchestration tools.

You will work closely with process owners, data engineers, and platform teams to identify high-value automation candidates, prototype quickly, and harden what works into supportable services.

Key Responsibilities

• Build and deploy end-to-end AI automations and agentic workflows that integrate LLMs with internal systems, APIs, and data sources.
• Design and implement RAG (retrieval-augmented generation) pipelines: chunking, embedding, vector search, re-ranking, and grounded response generation.
• Develop tool- and function-calling integrations so agents can act on systems of record (ticketing, ERP, document stores, internal APIs), including building MCP servers to expose internal systems as agent tools.
• Partner with business stakeholders to map current-state processes, quantify manual effort, and prioritize an automation backlog by ROI and feasibility.
• Write production Python services and jobs — API clients, data transformations, schedulers, event-driven handlers — with proper logging, retries, and error handling.
• Build prompt and model evaluation harnesses: golden datasets, regression tests, accuracy and hallucination checks, and pre-deployment quality gates.
• Instrument automations with observability and tracing so failures, cost spikes, latency regressions, and quality drift are detected early.
• Implement human-in-the-loop checkpoints, approval steps, and safe fallbacks for workflows where full autonomy is inappropriate.
• Optimize for cost and latency through model selection, caching, batching, prompt compression, and routing between larger and smaller models.
• Document architecture, runbooks, and handover material; support and iterate on automations after launch.
• Follow security, privacy, and responsible-AI requirements around data handling, access control, PII, and model usage.

Required Qualifications

• 3–5 years of professional software engineering, data engineering, or automation engineering experience, including at least 1 year building with LLMs or AI services.
• Strong Python skills — async programming, API integration, testing, packaging, and clean, maintainable code.
• Practical experience with LLM APIs (e.g. Anthropic Claude, OpenAI, Gemini, or Bedrock) in a real application, not just experimentation.
• Hands-on experience with at least one agent or orchestration framework (LangChain, LangGraph, LlamaIndex, CrewAI, Semantic Kernel, AutoGen, or equivalent in-house tooling).
• Demonstrated experience building RAG systems and working with a vector store (pgvector, Pinecone, Weaviate, Qdrant, Milvus, OpenSearch, or similar).
• Solid grasp of prompt engineering and context design, plus structured output techniques (JSON schemas, tool/function calling, validation and repair).
• Familiarity with the Model Context Protocol (MCP) — building MCP servers to expose internal systems as agent tools, and integrating them into agent applications.
• Strong REST/GraphQL API integration skills, including OAuth and token-based authentication, webhooks, pagination, and rate-limit handling.
• Working knowledge of SQL and data modeling; comfortable joining and cleaning data across systems.
• Experience with Git-based workflows, code review, and CI/CD.
• Cloud experience on AWS, Azure, or GCP — compute, serverless functions, queues, secrets management, and managed AI services.
• Containerization fundamentals with Docker.
• Proven ability to work directly with stakeholders: eliciting requirements, demoing prototypes, and managing expectations.

Preferred Qualifications

• Experience with workflow automation platforms: n8n, Zapier, Power Automate, Airflow, Temporal, Prefect, or Dagster.
• Background in RPA (UiPath, Automation Anywhere, Blue Prism) and experience modernizing RPA-era automations with AI.
• Experience with LLM observability and evaluation tooling (LangSmith, LangFuse, Arize, Braintrust, Ragas, or DeepEval).
• Multi-agent system design — planner/executor patterns, supervisor architectures, delegation, and shared state.
• Document intelligence and multimodal work: OCR, PDF parsing, table extraction, vision model usage.
• Frontend familiarity (React.js, Next.js, TypeScript) for building internal tools and demos.
• Infrastructure-as-code (Terraform, CloudFormation) and Kubernetes exposure.
• Experience with enterprise governance concerns: AI risk review, audit trails, data residency, and access-scoped retrieval.
• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.