AI Engineer

7 days ago

Noida, India Innovationm Full time

Position: AI Engineer

Exp:9 to 12 years

Location :Noida (WFO)

Role & responsibilities

Architect and build a reusable, secure, and scalable Agentic AI framework that can be adopted by multiple teams across the enterprise.
Define standards, patterns, and abstractions for building intelligent agents using LLMs and other foundational models.
Leverage and extend frameworks like LangGraph, CrewAI, or Semantic Kernel to enable advanced agentic capabilities such as (Long-horizon task planning, Tool calling (APIs, databases, RPA, internal services), Memory persistence and retrieval (via vector stores or knowledge graphs),Autonomous decision-making and reflection,Multi-agent orchestration and collaboration, Human-in-the-loop workflows,Monitoring and observability,Governance and compliance,Security and access control,Performance optimization and cost management
Collaborate with cross-functional teams to ensure the framework aligns with enterprise-grade expectations.
Integrate the framework with existing enterprise platforms.
Conduct research and stay updated on the latest advancements in Agentic AI, LLMs, and related technologies.
Mentor and guide junior engineers in best practices for building agentic systems.
Coach and guide engineering teams in adopting and extending the framework.

Requirements

4 -6  years of software engineering or AI/ML experience, with 2+ years in agentic or LLM-based system design.
Hands-on experience with LangGraph, CrewAI, AutoGPT, BabyAGI, or similar frameworks.
Proficiency in Python and common AI/LLM libraries (LangChain, OpenAI API, Hugging Face, etc.).
Strong grasp of agent lifecycle, orchestration, and autonomy patterns (e.g., ReAct, Plan-and-Execute, hierarchical agents).
Experience in integrating LLM agents with tools (APIs, search, file systems, databases) and managing secure, compliant interaction models.
Solid understanding of vector stores , memory architectures, and context management strategies.
Experience deploying agentic frameworks in large-scale enterprise environments (finance, telecom, healthcare, etc.) and Knowledge of LangGraph's stateful workflows will be a plus.

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