Role & responsibilities

- Design and architect scalable, secure, and production-ready AI/ML and Generative AI solutions aligned with business requirements.
- Translate complex business problems into AI-driven solution architectures, technical roadmaps, and implementation strategies.
- Lead the architecture and development of LLM, RAG, AI agent, NLP, computer vision, and machine learning solutions.
- Evaluate and select appropriate AI models, cloud services, frameworks, databases, and technology stacks.
- Design end-to-end AI architectures across AWS, Azure, or GCP, including data, compute, APIs, security, and deployment layers.
- Define best practices for MLOps/LLMOps, model deployment, monitoring, evaluation, scalability, reliability, and cost optimization.
- Work closely with data scientists, ML engineers, software engineers, DevOps teams, product managers, and business stakeholders.
- Develop technical prototypes and proof-of-concepts to validate AI solutions and demonstrate business value.
- Ensure AI solutions meet enterprise standards for security, privacy, governance, compliance, and responsible AI.
- Provide technical leadership, architecture guidance, code/design reviews, and mentorship to engineering teams.
- Collaborate with clients and senior stakeholders to understand requirements, present solution options, and communicate technical concepts clearly.
- Stay current with emerging AI/GenAI technologies and identify opportunities to integrate them into enterprise solutions.

Preferred candidate profile





- 5 to 10 years of experience in software engineering, AI/ML, cloud architecture, solution architecture, or related technology roles.
- Strong hands-on experience with AI/ML and Generative AI, particularly LLMs, RAG, AI agents, prompt engineering, and model evaluation.
- Strong programming skills in Python and familiarity with contemporary AI/ML frameworks and libraries.
- Proven experience designing and implementing cloud-native solutions on AWS, Azure, or GCP.
- Strong understanding of system design, distributed systems, microservices, APIs, databases, data pipelines, and enterprise architecture.
- Experience with MLOps/LLMOps, CI/CD, Docker, Kubernetes, model deployment, monitoring, and observability.
- Familiarity with vector databases, embedding models, LangChain/LlamaIndex, AI APIs, and open-source/commercial LLMs.
- Strong understanding of AI security, data privacy, governance, responsible AI, and enterprise integration patterns.
- Ability to convert business requirements into scalable technical architectures and clearly communicate solutions to technical and non-technical stakeholders.
- Demonstrated experience leading technical initiatives, PoCs, architecture decisions, and cross-functional engineering teams.
- Strong analytical, problem-solving, communication, consulting, and stakeholder-management skills.
- Relevant certifications in AWS/Azure/GCP, AI/ML, cloud architecture, or enterprise architecture are an advantage.

📌 AI Solutions Architect (India)
🏢 Good Co India
📍 India