AI Engineer III

Rekruton Technologies; Ahmedabad District; Presencial; INR 2 500 000 - 4 500 000; Tempo integral

Resumo da oferta

Rekruton Technologies in India seeks an experienced AI Architect to lead the design and implementation of complex AI systems, spanning models, retrieval, tools, agents, data, APIs, and existing services. You will own AI capabilities end-to-end from discovery through deployment and operations.

The role requires hands-on production experience with multiple model providers, tool calling, and scalable architectures, plus mentoring of engineers and collaboration with cross-functional teams.

Qualificações

• Proven experience delivering end-to-end AI systems.
• Ability to design scalable RAG architectures and retrieval strategies.
• Experience with multiple model providers and tool calling.
• Strong data, SQL and data platform background.

Responsabilidades

• Lead the design and implementation of complex AI systems spanning models, retrieval, tools, agents, data, APIs, and services.
• Own AI capabilities end-to-end from discovery to deployment and operations.
• Design scalable RAG and retrieval architectures, context and memory strategies, and model-provider integration patterns.
• Drive decisions on quality, latency, reliability, security, scalability and cost.
• Define evaluation strategies, datasets, and observability for production AI systems.
• Design resilience patterns including fallbacks, caching, retries, and safe degradation.
• Identify and build shared components standardized across pods.
• Lead technical design reviews and mentor AI Engineers I/II.
• Evaluate emerging models and frameworks and recommend adoption when valuable.

Conhecimentos

Advanced Python

Software design

Java integration

JavaScript/TypeScript

SQL

Snowflake

Data pipelines

Retrieval design

Ferramentas

LangChain

LlamaIndex

LangGraph

Descrição da oferta de emprego

What You'll Do

• Lead the design and implementation of complex AI systems spanning models, retrieval, tools, agents, data, APIs, and existing services.
• Own AI capabilities end-to-end from technical discovery and architecture through deployment, production operations, and continuous improvement.
• Design scalable RAG and retrieval architectures, context and memory strategies, agent/tool workflows, and model-provider integration patterns.
• Drive model and architecture decisions using systematic evaluation of quality, latency, reliability, security, scalability, and cost.
• Define evaluation strategies, golden datasets, regression suites, observability, and operational SLOs for production AI systems.
• Design resilience patterns including fallbacks, caching, retries, human review, failure recovery, and safe degradation.
• Identify and build shared components that should be standardized across pods rather than repeatedly implemented.
• Lead technical design reviews, document architecture and trade-offs, and partner with Staff Engineers/Solution Advisory Board oncross-platform implications.
• Mentor AI Engineers I/II and raise engineering quality through code/design reviews and technical coaching.
• Evaluate emerging models, protocols, frameworks, and platform capabilities and recommend adoption when they create measurable value.

Technical Skills & Technology Stack

• Programming & Systems: Advanced Python and strong software/system design fundamentals; ability to integrate with Java orJavaScript/TypeScript services as needed.
• Data: Strong SQL, Snowflake or equivalent data platforms, data pipelines, structured/unstructured data, and retrieval-oriented data design.
• Generative AI / LLMs: Deep hands-on experience with multiple model providers, tool/function calling, structured generation, contextengineering, and model-selection strategies.
• AI Architecture: Production RAG, vector/hybrid search, ranking, agents, tool orchestration, context/memory patterns, and human-in-the-loop workflows.
• Protocols & Frameworks: LangGraph/LangChain/LlamaIndex or equivalent; MCP/tool integration patterns; ability to design abstractionsindependent of individual frameworks/providers.
• Cloud & Platform: AWS or equivalent, containers, CI/CD, service observability, distributed system fundamentals, and production operations.
• Production AI: Evaluation frameworks, tracing/observability, guardrails, security, caching, fallbacks, latency/cost optimization, and failure-mode analysis.