Job Description

The Architect for AI Solutions is the primary technical visionary responsible for the end-to-end design, integration, and optimization of the company’s AI ecosystem. This role bridges the gap between complex software application layers, high-performance hardware infrastructure, and core business objectives.

In this start-up environment, you are not just a planner but a builder. You will define the technology roadmap, select the stack, and lead the development of MVPs to accelerate time-to-market. Your goal is to design modular, production-ready architectures that prevent technical debt and avoid expensive infrastructure mistakes while ensuring the system scales seamlessly from day one.

Job Requirements

AI Solution Vision & Architecture

• Strategic Requirement Mapping: Partner with stakeholders to gather requirements and translate ambiguous business problems into scalable, end-to-end AI system designs.
• Full-Stack Blueprinting: Define modular and production-ready architectures, selecting appropriate AI models (LLMs, VLMs, Deep Learning), integration frameworks, and data pipelines.
• Solution Alignment: Ensure every architectural decision aligns with long-term business objectives and the product roadmap.

1. Technology Stack & Data Strategy

• Stack Selection: Evaluate and select AI frameworks, databases, and infrastructure (Cloud, On-prem, or Hybrid).
• Setting Standards: Define enterprise standards for model development, APIs, middleware, data storage, and security to prevent technical debt.
• Data Architecture: Design the ingestion, storage, processing, and governance pipelines. Establish strategies for data quality, labeling, and training while ensuring strict compliance with privacy regulations.

1. Third-Party Integration & API Harmonization

• Ecosystem Onboarding: Evaluate, onboard, and manage the integration of third-party AI platforms, SaaS tools, and external data providers.
• API Architecture: Design and implement robust API strategies (REST, GraphQL, gRPC) to ensure seamless communication between internal services and external ecosystems.
• Harmonized Interoperability: Ensure that disparate third-party tools are successfully integrated into a "harmonized" architecture, maintaining system consistency, performance, and ease of maintenance.
• Vendor Lifecycle Management: Act as the technical lead for vendor assessments, ensuring third-party solutions meet the company’s scalability and security benchmarks.

1. Infrastructure & Deployment Design

• Hardware-Software Synthesis: Lead the design and oversight of infrastructure for AI training, inference, and scaling.
• Capacity Planning: Determine GPU, compute, and storage requirements. Conduct TCO analysis and create Bill of Materials (BOM) for on-premise AI clusters.
• Deployment Strategy: Decide between cloud, hybrid, edge AI, or on-premise deployments, ensuring compatibility across software stacks (Linux kernel tuning, driver installation).

1. Prototype & MVP Development

• Hands-on Execution: Lead the development of Proof-of-Concept (PoC) and MVP solutions.
• Engineering Collaboration: Work alongside engineering teams to integrate AI models, connect data sources, and build high-performance inference pipelines.
• Technical Validation: Rigorously validate the technical feasibility and performance of new AI products before full-scale rollout.

1. Security, Governance & Integration

• Security-by-Design: Embed security, encryption, and audit mechanisms into the AI platform from the ground up.
• System Interoperability: Architect integrations with enterprise systems, third-party APIs, and hardware/IoT devices to ensure future expansion.
• Compliance: Ensure the platform adheres to data protection regulations and industry-standard AI governance frameworks.

1. Cross-Functional Leadership

• Technical Bridge: Act as the "North Star" between business, product, and engineering teams, resolving complex architectural roadblocks.
• Mentorship: Provide guidance to developers, ML engineers, and DevOps teams through design reviews and technical decision-making.

Requirements

• Education: Bachelor’s or Master’s degree in Computer Science, AI, Data Engineering, or a related field.
• Experience: 10+ years in software architecture and systems engineering, with at least 3+ years specifically focused on production-grade AI/ML environments.
• Integration Expertise: Proven track record of successfully integrating complex third-party APIs and platforms into unified enterprise architectures.
• Startup Mindset: Proven ability to build the first working iteration of a system and thrive in a fast-paced, "builder" environment.
• Technical Breadth: Expert-level knowledge of AI frameworks, API design patterns, GPU acceleration, and Kubernetes.
• Advisory Skills: Exceptional ability to articulate complex concepts to non-technical stakeholders.

Technical Proficiencies

Skills & Technologies

• AI & Software: LLMs, VLMs, Prompt Engineering, Hugging Face, Deep learning and ML, Finetuning and infernece, Python (FastAPI/Django), Node.js, React/Vue.js.
• AI Infrastructure: NVIDIA GPU Clusters, InfiniBand/NVLink, HPE/Dell/Supermicro AI Servers.
• Data & Storage: PostgreSQL, MongoDB, Redis, Parallel File Systems (Lustre/GPFS), S3-compatible Object Storage, RAID configurations.
• DevOps & MLOps: Docker, Kubernetes (K8s), CI/CD (GitHub Actions/GitLab CI), Linux Mastery (RHEL/Ubuntu Server), CUDA Toolkit.

Skills