As an Agentic AI Architect at Acuvate Software Solutions & Services, you will lead the architectural vision, design, and implementation of enterprise-grade agentic AI solutions that drive digital transformation across industries. You’ll collaborate with Product, Data Engineering, and Cloud teams to build autonomous AI agents, orchestrated multi-agent workflows, and scalable AI systems that integrate with clients’ core business platforms. This role sits at the intersection of architecture, data science, cloud engineering, and AI system design, with direct impact on how Acuvate helps customers transform operations using intelligent agents & AI-driven workflows.
Architecture & System Design:
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Architect enterprise-grade agentic AI systems and multi-agent ecosystems that can plan, reason, and execute multi-step business tasks.
Design scalable, secure, and resilient AI reference architectures that leverage LLMs, orchestration layers, memory systems, and tool integration modules.Define AI agent lifecycle frameworks (development deployment monitoring- governance).
Modeling & Implementation:
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Lead development and deployment of Generative AI models and autonomous agent workflows for production use-cases.
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Build RAG (Retrieval Augmented Generation), reasoning pipelines, prompt libraries, and framework-based orchestration using tools like LangChain, LlamaIndex, or similar frameworks.
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Collaborate cross-functionally to integrate AI agents into enterprise platforms via APIs, SDKs, and microservices.
Technical Leadership & Innovation:
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Stay updated with state-of-the-art technologies (LLMs, reinforcement learning, multi-agent systems) and introduce best practices into solutions.
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Mentor engineers and data scientists in architectural patterns, agentic solution design, and deployable AI workflows.
Lead pilot Proof-of-Concepts (PoCs) to validate feasibility and scale high-impact AI systems.
Governance, Ethics & Performance:
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Establish AI governance standards, security baselines, and compliance mechanisms to address reliability and ethical considerations in agentic systems.
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Define performance monitoring, lifecycle metrics, and validation approaches for production AI agents.
Stakeholder Engagement:
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Partner with product owners, clients, and engineering stakeholders to translate business challenges into technical architectures.
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Present architectural frameworks, solution roadmaps, and AI strategies to both technical and non-technical audiences.
Educational Background:
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Bachelor’s or master’s degree in computer science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field.
Ph.D. in relevant domain is a strong plus.
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4+ years’ experience in AI/ML systems, including 2+ years with Generative AI and agentic workflows.
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Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow).
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Hands-on experience with LLM fine-tuning, prompt engineering, RAG pipelines, and multi-agent orchestration.
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Experience with AI frameworks/tools such as LangChain, LlamaIndex, Hugging Face, OpenAI, Anthropic, AutoGen, or similar.
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Familiarity with cloud platforms & AI services (AWS, Azure, GCP) and MLOps/LLMOps practices.
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Strong knowledge of APIs, microservices, scalable distributed systems, and data ecosystem components (vector databases, data lakes).
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Excellent problem-solving, analytical, and research capabilities.
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Strong communication skills with ability to articulate technical concepts to both technical & business stakeholders.
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Leadership and mentoring experience.
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Experience with multi-modal AI systems (text, image, audio) or knowledge graphs & reasoning engines.
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Prior work in cross-industry digital transformation initiatives.
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Contributions to open-source communities, publications, or AI research initiatives.
