AI Architect

Job Description:

Role: AI Architect

Exp: 8+ Years

Compliance: PF - No, BGV – Yes

Work Mode: Remote/ resource must visit the client office during the time of onboarding, which is ideally 2 -3 Days / if there is any customer visit, then the resource needs to be present.

Job Summary:

We are seeking an AI Architect who will play a critical role in shaping, defining, and enabling AI adoption across the enterprise. This role will bridge business strategy and technical execution, translating business and technology use cases into scalable, secure, and well‑governed AI reference architectures.

The AI Architect will partner closely with business leaders, product teams, data engineering, platform, security, and enterprise architecture teams to ensure AI solutions deliver measurable business value while adhering to architectural and governance standards.

Key Responsibilities

1. AI Use Case Definition & Value Articulation

• Engage with business stakeholders to identify, assess, and prioritize AI/ML use cases across domains (e.g., operations, customer experience, risk, finance, HR, supply chain, IT).
• Articulate business value, outcomes, and KPIs for AI initiatives, including productivity gains, cost optimization, revenue enablement, and customer impact.
• Define technology‑driven use cases, such as intelligent automation, AIOps, predictive maintenance, anomaly detection, knowledge assistants, and developer productivity.
• Translate high‑level business problems into AI‑amenable problem statements and solution approaches.

2. AI Reference Architecture & Solution Design

• Design and maintain end‑to‑end reference architectures for AI workloads, covering:
• Data ingestion and engineering
• Feature stores and data pipelines
• Model development, training, and experimentation
• Model deployment, inference, and serving
• Monitoring, observability, and retraining
• Define architectural patterns for:
• Classical ML, deep learning, and GenAI / LLM‑based solutions
• Batch vs. real‑time inference
• Cloud, hybrid, and on‑premises AI workloads
• Ensure architectures comply with enterprise standards for scalability, resilience, security, privacy, and cost efficiency.

3. Platform, Tools & Technology Strategy

• Define the AI/ML platform strategy, including preferred tools, frameworks, and services (open source, commercial, and cloud‑native).
• Provide architectural guidance on:
• MLOps and LLMOps practices
• Model lifecycle management
• CI/CD integration for AI workloads
• Evaluate emerging AI technologies and make build vs. buy vs. partner recommendations.

4. Governance, Security & Responsible AI

• Embed Responsible AI principles into architecture designs, including fairness, explainability, transparency, and ethical use.
• Ensure compliance with data privacy, regulatory, and security requirements.
• Collaborate with risk, compliance, and security teams to define AI governance guardrails and architectural controls.

5. Enablement & Collaboration

• Act as a thought leader and trusted advisor to engineering and product teams on AI architecture and best practices.
• Create and maintain architecture artifacts, blueprints, standards, and decision records.
• Support engineering teams during solution design and critical implementation phases.
• Contribute to AI literacy and capability building across the organization.

Required Qualifications

Education

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.

Experience

• 8+ years of experience in software, data, or enterprise architecture, with hands‑on experience in AI/ML solutions.
• Proven experience defining AI use cases and translating them into production‑grade architectures.
• Experience working in large, complex, enterprise environments.

Technical Skills

• Strong understanding of:
• Machine learning concepts, deep learning, and generative AI
• Data architecture, pipelines, and analytical platforms
• Experience with:
• Cloud platforms (Azure, AWS, or GCP) and their AI/ML services
• MLOps / LLMOps tools and practices
• APIs, microservices, and event‑driven architectures
• Familiarity with:
• Model monitoring, drift detection, and performance management
• Security and data privacy considerations for AI systems

Business & Soft Skills

• Strong ability to communicate complex AI concepts to non‑technical stakeholders.
• Excellent stakeholder management and facilitation skills.
• Strategic mindset with a strong focus on business outcomes and value realization.
• Ability to operate at both strategic and hands‑on architectural levels.

Nice to Have

• Experience with industry‑specific AI use cases in the Payments and Finance Industry.
• Exposure to AI governance frameworks and responsible AI initiatives.
• Certifications in Data or AI/ML.
• Prior experience establishing or scaling an enterprise AI capability or center of excellence.