Generative AI Specialist - Agentic AI

Position Title : Generative AI Specialist Agentic AI

Experience Required :

- 6+ years of experience as a Data Scientist / AI Engineer

- 2+ years of hands-on experience in Generative AI solution development

Job Summary :

We are seeking a highly skilled Generative AI Specialist with expertise in Agentic AI systems, multi-agent orchestration, and autonomous AI workflows. The ideal candidate will have strong experience in designing, developing, and deploying intelligent AI agents capable of autonomous decision-making, collaboration, planning, and reasoning in complex enterprise environments.

You will work on cutting-edge AI solutions leveraging LLMs, multi-agent frameworks, and advanced orchestration architectures to build scalable, production-grade GenAI applications.

Key Responsibilities :

- Design, develop, and deploy advanced Generative AI and Agentic AI solutions for enterprise use cases.

- Build autonomous and collaborative AI agents capable of planning, reasoning, tool usage, and decision-making.

- Develop multi-agent systems with coordinated workflows using frameworks such as AutoGen, LangGraph, LangChain, and CrewAI.

- Integrate Large Language Models (LLMs) such as GPT, LLaMA, Mistral, and similar models into intelligent agent ecosystems.

- Implement hierarchical and distributed agent architectures for scalable AI orchestration.

- Design memory architectures, context management systems, and action-planning mechanisms for AI agents.

- Develop AI agents that can independently execute tasks, collaborate with other agents, and dynamically adapt to changing environments.

- Optimize agent collaboration, negotiation, and communication protocols to improve workflow efficiency.

- Ensure governance, reliability, observability, and ethical AI practices within decentralized AI ecosystems.

- Collaborate with cross-functional teams including Data Science, Engineering, Product, and Business stakeholders to deliver AI-driven solutions.

- Monitor and improve AI system performance, scalability, and operational efficiency.

Required Skills & Qualifications :

Technical Skills :

- Strong experience in Python and AI/ML development.

- Deep understanding of Agentic AI principles, including :

a. Autonomous agents

b. Goal-driven systems

c. Self-improving agents

d. Self-organizing agents

e. Distributed intelligence

- Expertise in multi-agent orchestration frameworks :

a. AutoGen

b. LangGraph

c. LangChain

d. CrewAI

- Hands-on experience with LLM integration and prompt engineering.

- Strong understanding of :

a. Agent memory architectures

b. Tool calling and tool integration

c. Planning and reasoning systems

- Retrieval-Augmented Generation (RAG)

- AI workflow orchestration

- Experience with vector databases, embeddings, and semantic search systems.

- Knowledge of cloud platforms such as AWS, Azure, or GCP.

- Experience with API integrations, microservices, and scalable AI deployments.

- Familiarity with MLOps / LLMOps practices and CI/CD pipelines for AI systems.

Preferred Qualifications :

- Experience with decentralized AI governance and distributed agent coordination.

- Knowledge of reinforcement learning, autonomous systems, or cognitive architectures.

- Exposure to AI safety, alignment, and responsible AI practices.

- Experience working with enterprise-grade GenAI applications and production deployments.

Key Performance Indicators (KPIs) :

- Autonomy Score Measures the degree of independent decision-making by AI agents.

- Collaboration Efficiency Evaluates how effectively agents coordinate and exchange information.

- Task Completion Rate Tracks the percentage of tasks successfully executed by agents.

- Reasoning Accuracy Measures the quality and relevance of AI-generated outputs and decisions.

- Workflow Optimization Assesses improvements in automation efficiency and operational productivity.