AI Engineer II

Rekruton Technologies; Ahmedabad District; Presencial; INR 1.200.000 - 2.400.000; Jornada completa

Descripción de la vacante

Rekruton Technologies seeks an experienced AI Engineer to design and deploy AI-powered features and production LLM applications. You will build retrieval pipelines, integrate models from multiple providers, and implement observability and cost controls in a fast-paced environment.

You will mentor teammates, participate in design reviews, and contribute to AI engineering standards while working with Python, SQL, and cloud data platforms.

Formación

• Strong Python and software engineering fundamentals.
• Experience with major model APIs, structured outputs, and prompt design.
• Knowledge of SQL and cloud data platforms (e.g., Snowflake).
• Hands-on in building AI-powered systems with LLMs and tool calling.
• Experience with retrieval, embeddings, and vector/hybrid search.

Responsabilidades

• Independently design, build, deploy, and operate AI-powered features, APIs, services, and workflows.
• Develop production LLM applications with structured outputs, tool calling, and context-management patterns.
• Design retrieval pipelines across structured and unstructured data, including chunking, embeddings, and grounding strategies.
• Evaluate and integrate models from multiple providers for quality, latency, reliability, security, and cost.
• Build reusable components for prompts, tools, retrieval, and evaluations; flag cross-pod standardization candidates.
• Define automated evaluations and regression tests for AI quality and grounding.
• Implement production controls: observability, caching, retries, fallbacks, rate limits, and cost controls.
• Troubleshoot production issues to improve reliability, latency, and operating cost.
• Participate in design reviews and contribute to AI engineering standards.
• Support and mentor teammates through reviews and knowledge sharing.

Conocimientos

Python

Java

JavaScript/TypeScript

SQL

Snowflake

LLMs

RAG

Embeddings

Vector search

Retrieval

Agentic workflows

Context management

Memory patterns

LangGraph

LangChain

LlamaIndex

REST APIs

JSON

Docker

Git

CI/CD

Herramientas

LangGraph

LangChain

LlamaIndex

Docker

Git

CI/CD

Descripción del empleo

What You'll Do

• Independently design, build, deploy, and operate AI-powered features, APIs, services, and workflows.
• Develop production LLM applications using structured outputs, tool/function calling, RAG, agentic workflows, and context-management patterns.
• Design retrieval pipelines across structured and unstructured enterprise data, including chunking, embeddings, vector/hybrid search, ranking, andgrounding strategies.
• Evaluate and integrate models from multiple providers based on quality, latency, reliability, security, and cost trade-offs.
• Build reusable components within your pod's codebase for prompts, tools, retrieval, and evaluations, and flag candidates for cross-pod standardization.
• Define and maintain automated evaluations and regression tests for AI quality, groundedness, task success, and failure modes.
• Implement production controls including observability, caching, retries, fallbacks, rate limits, human-in-the-loop flows, and cost controls.
• Troubleshoot production issues and improve reliability, latency, quality, and operating cost using measurable signals.
• Participate in technical design reviews and contribute to AI engineering standards and documentation.
• Support and mentor AI Engineer I teammates through reviews, pairing, and knowledge sharing.

Technical Skills & Technology Stack

• Programming: Strong Python and software engineering fundamentals; working knowledge of Java or JavaScript/TypeScript is useful for platformintegration.
• Data: Strong SQL; Snowflake or equivalent cloud data platforms; structured and unstructured data processing.
• Generative AI / LLMs: Hands-on experience with major model APIs, structured outputs, tool/function calling, model selection, and prompt/contextdesign.
• AI Application Development: RAG, embeddings, vector/hybrid search, retrieval/ranking, agentic workflows, context management, and basic memorypatterns.
• Frameworks: Practical experience with LangGraph, LangChain, LlamaIndex, or equivalent orchestration libraries; ability to work without frameworklock-in.
• APIs & Integration: REST APIs, JSON, asynchronous/service integration patterns; familiarity with MCP and tool ecosystems is preferred.
• Cloud & Engineering: AWS or equivalent cloud, Docker, Git, CI/CD, automated testing, logging, and production debugging.
• Production AI: Evaluations, observability/tracing, guardrails, caching, fallbacks, latency optimization, and token/cost management.