Job
Description
Positions: Tech Lead Location: Remote, India-Based Role: Full-Time Role Requirements Educational Background Bachelors degree in Computer Science, AI/ML, Information Systems, or a related technical field. Masters degree (MSAI, MBA, or MS in Cybersecurity/Engineering) preferred. Industry certifications: CISSP, AWS Certified Solutions Architect, Azure AI Engineer, TOGAF, or similar. Experience Requirements 15+ years of progressive technology leadership experience. 10+ years in infrastructure, AI/ML systems, or enterprise architecture. 7+ years in intelligent automation, RPA, or AI/ML deployment at scale. 5+ years in a senior leadership role (VP of Engineering, Head of Architecture). Core Competencies & Skills AI/ML & Intelligent Automation Design Cloud Infrastructure & Hybrid Architecture (AWS, Azure, GCP) Cybersecurity & Risk Governance (Zero Trust, SOX, GDPR, ISO 27001) DevOps & MLOps Pipelines Systems Integration (ERP, RPA, APIs) Data Engineering & Real-Time Analytics Infrastructure Strategic Leadership & Client Advisory Act as interface with AI/GenAI partners Overseeing Mobile App Development Key Responsibilities 1. Technology Strategy Define and lead the firm's technology roadmap. Champion AI/ML innovation, from prototype to production, across industries with a strong focus on business innovation. 2. Cybersecurity Leadership Own the security architecture for solutions, including secure model inference, data protection, and client compliance. Champion AI guardrails, performance monitoring and ethics for AI solutions Implement comprehensive cybersecurity standards and controls, including IAM, threat monitoring and response. Design and implement process standards and attain/maintain respective organizational certifications. 3. Governance, Compliance & Risk Ensure all AI solutions meet legal, ethical, and compliance standards. Lead governance of AI usage including model explainability, auditability, and bias mitigation. 4. Scalable Infrastructure Architecture Build and govern modular, secure infrastructure to support workflows, including model training, deployment, and monitoring. Leverage containerization (Docker, Kubernetes) and serverless architectures for rapid, scalable delivery. 5. Technical Delivery Consulting and Delivery Oversight Translate client goals into secure, scalable AI automation blueprints with measurable outcomes. Technical quality oversight for AI-powered automation solutions and/or AI integrations with other systems. Example include, GenAI applications and agentic process automation. Act as technical advisor for high-value client engagements.
