I

AI Intern

IXIGOGurugram, India5h ago
onsitefull-timeentry
210 views0 applicants

Job Description

We are looking for highly motivated AI Interns with a strong foundation in AI/ML, passionate about applying agentic AI to solve business and enterprise-focused challenges. This is an exciting opportunity to work on real-world B2B processes, collaborate with experienced engineers and data scientists, and gain hands-on experience in building intelligent, scalable solutions. Candidates from premier institutes such as IITs, NITs, DTU, NSIT, and IIITs are preferred. Key Responsibilities: Agentic AI automation: Design LLM-powered, agentic workflows that plan multi-step tasks, call tools/APIs/DBs, use memory, and self-check results with human-in-the-loop review where needed. Browser & RPA automation: Build headless browser bots (e.g., Playwright/Browser-use/Selenium) to extract/submit data across enterprise web apps, handle auth/session management, pagination, retries, and failure recovery—while respecting security and ToS. Data pipelines: Build reliable ingestion and transformation pipelines (Python/SQL, dbt/Pandas), schedule with Prefect/Airflow/Dagster, and deliver clean datasets to downstream automations and dashboards. Workflow orchestration: Compose multi-stage automations (agents + pipelines + RPA), manage state, queues, and idempotency; implement fallbacks and circuit breakers. Quality & guardrails: Add validation, schema checks, structured output parsing, prompt/tool guards, and test coverage (unit/integration/e2e) to ensure accuracy and safety. Observability & cost control: Instrument logging, tracing, and metrics (throughput, success rate, latency, cost per task); tune prompts, caching, and batching for performance and spend. Continuous learning: Track advances in Agentic AI, RPA, workflow tools, and LLM ops; propose pragmatic upgrades. Qualifications Qualifications Strong Python and SQL; solid data structures/algorithms. Exposure to at least one of: Browser Automation, RPA: Playwright, Browser-use, Selenium etc Orchestration: Prefect, Airflow or Dagster Microservices: Fastapi, REST/GraphQL LLM Tooling: LLM orchestration, function/tool calling, retrieval Comfort with Git, Docker, and writing clean, testable code. What You’ll Gain Hands-on experience shipping agentic AI systems that automate real revenue-impacting workflows. End-to-end ownership: from process discovery → pipeline → agent/RPA build → deployment → monitoring. Mentorship from engineers and data scientists delivering scalable automation in enterprise environments.

Requirements

  • Strong Python and SQL; solid data structures/algorithms.
  • Exposure to at least one of:
  • Browser Automation, RPA: Playwright, Browser-use, Selenium etc
  • Orchestration: Prefect, Airflow or Dagster
  • Microservices: Fastapi, REST/GraphQL
  • LLM Tooling: LLM orchestration, function/tool calling, retrieval
  • Comfort with Git, Docker, and writing clean, testable code.
  • What You’ll Gain
  • Hands-on experience shipping agentic AI systems that automate real revenue-impacting workflows.
  • End-to-end ownership: from process discovery → pipeline → agent/RPA build → deployment → monitoring.

Key Responsibilities

  • Agentic AI automation: Design LLM-powered, agentic workflows that plan multi-step tasks, call tools/APIs/DBs, use memory, and self-check results with human-in-the-loop review where needed.
  • Browser & RPA automation: Build headless browser bots (e.g., Playwright/Browser-use/Selenium) to extract/submit data across enterprise web apps, handle auth/session management, pagination, retries, and failure recovery—while respecting security and ToS.
  • Data pipelines: Build reliable ingestion and transformation pipelines (Python/SQL, dbt/Pandas), schedule with Prefect/Airflow/Dagster, and deliver clean datasets to downstream automations and dashboards.
  • Workflow orchestration: Compose multi-stage automations (agents + pipelines + RPA), manage state, queues, and idempotency; implement fallbacks and circuit breakers.
  • Quality & guardrails: Add validation, schema checks, structured output parsing, prompt/tool guards, and test coverage (unit/integration/e2e) to ensure accuracy and safety.
  • Observability & cost control: Instrument logging, tracing, and metrics (throughput, success rate, latency, cost per task); tune prompts, caching, and batching for performance and spend.
  • Continuous learning: Track advances in Agentic AI, RPA, workflow tools, and LLM ops; propose pragmatic upgrades.
  • Qualifications
  • Qualifications
  • Strong Python and SQL; solid data structures/algorithms.

About IXIGO

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