MS-FDL-AI Architect-Manager

Role Overview:

We are seeking a highly motivated Manager – Data & Analytics with a minimum of 8 years of experience to lead cross-functional teams in designing, developing, and deploying next-generation AI solutions. This role requires a blend of technical expertise in building GenAI and Agentic AI workflows, strong project management skills, and the ability to manage client relationships and business outcomes. The manager will oversee the delivery of AI-driven solutions, ensure alignment with client needs, and foster a high-performance culture within the team.

Key Responsibilities:

Leadership & People Management

• Lead, mentor, and grow a team of AI engineers, data scientists, and solution architects.
• Define team goals, set performance expectations, and conduct regular feedback and evaluations.
• Foster collaboration, innovation, and knowledge-sharing within the team.
• Drive adoption of best practices in AI development, MLOps, and project execution.

Project Management

• Own end-to-end delivery of AI projects—from scoping and planning to deployment and monitoring.
• Define project roadmaps, allocate resources, and track progress against milestones.
• Manage risks, dependencies, and escalations while ensuring timely delivery.
• Coordinate with cross-functional stakeholders including product, design, data, and engineering teams.

Client & Stakeholder Management

• Act as the primary point of contact for clients, ensuring strong relationships and trust.
• Translate client business problems into AI solutions using GenAI and agentic AI frameworks.
• Prepare and deliver executive-level presentations, demos, and progress updates.
• Negotiate scope, timelines, and deliverables with clients and internal stakeholders.

AI Strategy & Innovation

• Shape and refine the strategy for AI adoption, focusing on generative and agentic workflows.
• Identify new opportunities for AI-driven automation, augmentation, and decision support.
• Evaluate emerging technologies, frameworks, and tools to enhance solution effectiveness.
• Partner with business leaders to define success metrics and measure ROI of AI initiatives.

Architect & Build Scalable ML/LLM Solutions

• Design, implement, and optimize end-to-end machine learning and LLM pipelines, ensuring robustness, scalability, and adaptability to changing business requirements.
• Drive best-in-class practices for data curation, system reliability, versioning, and automation (CI/CD, monitoring, data contracts, compliance) for ML deployments at scale.
• Lead the adoption of modern LLMOps and MLOps tooling and principles such as reproducibility, observability, automation, and model governance across our cloud-based infrastructure (AWS, Snowflake, dbt, Docker/Kubernetes).
• Initiate and Own Projects from Conception to Production
• Identify and assess new business opportunities where applied machine learning or LLMs create impact. Rapidly move concepts from prototype to full production solutions enabling measurable business value.
• Collaborate closely with product, engineering, analytics, and business stakeholders to define requirements, set measurable goals, and deliver production-grade outcomes.
• Serve as a technical thought leader and trusted advisor, explaining complex architectural decisions in clear, actionable terms to both technical and non-technical stakeholders.
• Evangelize engineering best practices and frameworks that enable high-quality delivery, system reliability, and strong data governance.
• Translate analytical insights into business outcomes, helping organizations realize the ROI of AI/ML investments.

Qualifications & Skills:

• Education: Bachelor’s/Master’s degree in Computer Science, Data Science, AI/ML, or related field.
• Experience: 8–12 years of total experience, with at least 3–5 years in AI/ML leadership roles.
• Proven expertise in Generative AI (LLMs, text-to-X, multimodal) and Agentic AI (orchestration frameworks, autonomous agents, tool integration).
• Strong understanding of cloud platforms (AWS/Azure/GCP) , LLMOps , and AI governance.
• Demonstrated ability to manage large-scale AI/ML projects with multi-stakeholder involvement.
• Exceptional communication and client engagement skills.
• Experience in people leadership—coaching, mentoring, and building high-performing teams.
• Strong business acumen with the ability to balance innovation with delivery discipline.