Design and deploy scalable AI systems, particularly Generative AI and LLM-based applications. Collaborate with engineering, product, and business teams to shape technical foundations for next-generation AI-driven products. Strong expertise in AI architecture, distributed systems, and cloud-native platforms required.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
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Job Description
EazyML, recognized by Gartner, (www.EazyML.com) specializes in Responsible AI. Our solutions enable proactive compliance and sustainable automation for enterprises adopting AI at scale. The company is also associated with breakthrough startups like Amelia.ai.
This is a Fully Remote position in INDIA, you can work from anywhere in India. We are looking for an AI Software Architect with strong experience designing and deploying scalable AI systems, particularly Generative AI and LLM-based applications. The ideal candidate will have deep expertise in AI architecture, distributed systems, and cloud-native platforms, and will play a key role in shaping the technical foundation for next-generation AI-driven products.
This role requires strong collaboration with engineering, product, and business teams to design robust AI architectures that align with organizational goals while ensuring scalability, performance, and responsible AI practices.
Required Qualifications- 4+ years of experience in software engineering or architecture roles with strong exposure to AI/ML systems.
- Strong knowledge of modern neural network architectures such as Transformers, CNNs, and RNNs.
- Experience designing scalable and distributed architectures for AI-powered applications.
- Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud.
- Experience with containerization and orchestration technologies including Docker and Kubernetes.
- Strong understanding of microservices architecture, RESTful APIs, and distributed system design.
- Experience working with MLOps / LLMOps pipelines including model training, deployment, monitoring, and lifecycle management.
- Familiarity with large-scale data systems and modern database technologies.
- Experience translating business requirements into scalable AI solution architectures.
- Strong documentation skills for architecture designs, workflows, and technical decision-making.
- Comfortable working in a startup or fast-paced environment with strong ownership and leadership mindset.
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Architect and oversee the development of scalable generative AI systems and enterprise-grade AI platforms. Design robust architectures that support model training, inference, monitoring, and lifecycle management in production environments. Guide the selection, customization, and optimization of state-of-the-art generative AI and large language models.
Design and implement APIs, microservices, and integration frameworks to embed AI capabilities into enterprise applications. Ensure AI platforms meet high standards for performance, reliability, security, and scalability, while adhering to data governance and privacy requirements.
Collaborate with product, engineering, and business stakeholders to define technical requirements and AI architecture strategies. Design end-to-end pipelines for AI model deployment and monitoring, ensuring seamless integration into existing systems.
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Lead architectural decisions for LLM applications, AI workflows, and distributed AI infrastructure. Define best practices for responsible AI development, including strategies to mitigate risks such as model hallucinations, bias, and reliability issues.
Provide technical leadership and mentorship to engineering teams while contributing to long-term technology strategy and AI platform evolution.
Preferred Qualifications- Experience working with Generative AI frameworks and orchestration tools such as LangChain, LangGraph, or similar platforms.
- Experience with prompt engineering, LLM fine-tuning techniques (LoRA, RLHF, PEFT), and model optimization strategies.
- Familiarity with performance optimization for AI workloads, including GPU/TPU acceleration, quantization, pruning, or model distillation.
- Experience with AI observability and monitoring tools for tracking model performance, drift, and anomalies.
- Knowledge of AI governance, security, and compliance frameworks such as GDPR or SOC 2.
Prior experience building enterprise-scale AI or LLM-based products.
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