AI/ML Solution Architect

Bacancy Technology; Ahmedabad District; On-site; INR 4,200,000 - 6,000,000; Full time

Job summary

Bacancy Technology is seeking an experienced Technical Architect AI/ML to design scalable AI-powered software solutions. The role combines technical depth with leadership to guide architecture decisions across teams and client engagements.

You will drive end-to-end architecture definitions, select frameworks, oversee design reviews, and ensure security, performance, and reliability in complex enterprise systems.

Qualifications

• 12+ years in software engineering or architecture.
• Experience designing enterprise-grade architectures.
• Strong AI/ML and Generative AI knowledge.
• Experience with LLMs, embeddings, vector databases.
• Proficient in Python and APIs.

Responsibilities

• Design end-to-end technical architectures for enterprise apps with AI/ML capabilities.
• Translate business requirements into scalable, secure tech solutions.
• Define HLD, LLD, architecture patterns, tech stacks, APIs, data flows.
• Architect AI/ML-enabled apps with Generative AI, LLMs, NLP, ML, DL.
• Design AI/ML systems with model selection, inference, evaluation, monitoring.
• Define MLOps/LLMOps architecture for production workloads.
• Address security, privacy, governance, cost, and reliability in designs.

Skills

Enterprise architecture

AI/ML knowledge

Python

Cloud platforms

Leadership

Security & privacy

API design

MLOps awareness

Tools

Docker

Kubernetes

CI/CD

Git

LLMOps

Job description

About the Role

Bacancy is looking for an experienced Technical Architect AI/ML to design and drive the architecture of scalable, secure, high-performance software solutions powered by Artificial Intelligence and Machine Learning.

The ideal candidate will have 12+ years of experience in software engineering and technical architecture, with strong expertise in designing enterprise-grade applications and a deep understanding of AI/ML, Generative AI, LLMs, cloud technologies, distributed systems, APIs, microservices, and modern software architecture.

The role requires a strong combination of technical depth, architectural thinking, hands-on problem-solving, and technical leadership. You will work closely with engineering teams, technology leadership, and clients to define technical strategies and transform complex business requirements into scalable technology solutions.

Key Responsibilities

• Design and define end-to-end technical architectures for complex enterprise applications with AI/ML capabilities.
• Translate business and functional requirements into scalable, secure, and maintainable technical solutions.
• Define High-Level Design (HLD), Low-Level Design (LLD), architecture patterns, technology stacks, APIs, integrations, and data flows.
• Architect AI/ML-enabled applications incorporating Generative AI, LLMs, NLP, Computer Vision, Machine Learning, and Deep Learning.
• Design solutions involving LLMs, RAG, embeddings, vector databases, AI agents, model inference, fine-tuning, and AI orchestration.
• Evaluate emerging technologies and select appropriate frameworks, platforms, tools, and cloud services.
• Define scalable architectures using microservices, event-driven architecture, distributed systems, serverless, and API-driven architectures.
• Design cloud-native solutions across AWS, Microsoft Azure, and/or Google Cloud Platform.
• Establish technical standards, architecture principles, coding practices, security standards, and engineering best practices.
• Work closely with developers, AI/ML engineers, data scientists, DevOps engineers, and product teams to ensure architectural alignment.
• Conduct architecture reviews, code/design reviews, technical assessments, and technology evaluations.
• Identify technical risks, scalability challenges, performance bottlenecks, and architectural dependencies and define mitigation strategies.
• Lead technical POCs and prototypes to validate architecture and technology choices.
• Ensure solutions are designed for security, scalability, reliability, performance, observability, and maintainability.
• Mentor and guide senior developers, technical leads, architects, and engineering teams.
• Participate in client-facing technical discussions, workshops, architecture presentations, and solution discussions.
• Contribute to pre-sales, technical proposals, estimations, solution approaches, and technical roadmaps when required.

AI/ML Responsibilities

• Architect and guide implementation of Generative AI and LLM-based solutions.
• Design enterprise RAG architectures, including document ingestion, chunking, embeddings, retrieval, reranking, and response generation.
• Define architecture for AI Agents and Agentic AI workflows.
• Evaluate and integrate models from platforms such as OpenAI, Azure OpenAI, Anthropic, Google, Hugging Face, and other relevant providers.
• Design AI/ML systems with appropriate model selection, inference, evaluation, monitoring, and lifecycle management.
• Define MLOps/LLMOps architecture for deploying and managing AI/ML workloads in production.
• Address AI-specific concerns including hallucination, model evaluation, security, privacy, governance, cost optimization, and responsible AI.
• Work with data teams to define data pipelines and architectures required for AI/ML applications.

Required Technical Skills

• 12+ years of experience in software engineering, technical architecture, or solution architecture.
• Strong experience designing enterprise-scale software architectures.
• Strong knowledge of Software Architecture, System Design, Design Patterns, SOLID principles, distributed systems, and scalability.
• Strong understanding of AI/ML and Generative AI technologies.
• Hands-on experience with LLMs, RAG, embeddings, vector databases, prompt engineering, AI agents, and model integration.
• Strong programming experience in Python and/or other modern programming languages.
• Experience with REST APIs, GraphQL, microservices, event-driven architecture, messaging, and system integrations.
• Strong knowledge of databases, data platforms, caching, search, and distributed data systems.
• Experience with AWS, Azure, and/or GCP.
• Experience with Docker, Kubernetes, CI/CD, Git, and DevOps practices.
• Understanding of MLOps/LLMOps and production deployment of AI/ML systems.
• Strong understanding of application security, authentication, authorization, data privacy, and cloud security.
• Experience designing highly available, fault-tolerant, and performance-optimized systems.