Technical Solutions Manager – AI Architect

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Kyndryl

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 2021

🏢 Enterprise

🔒 Cybersecurity

☁️ SaaS

Enterprise • Cybersecurity • SaaS

Kyndryl is a leading IT infrastructure services provider, serving thousands of enterprise customers worldwide. The company specializes in designing, building, managing, and modernizing complex, mission-critical information systems. Kyndryl offers a range of services including IT consulting, cloud services, cybersecurity, data and AI solutions, and digital workplace transformation. With a strong focus on innovation, partnerships, and co-creation, Kyndryl helps businesses tackle IT complexity and drive operational excellence. The company operates across various industries such as automotive, healthcare, banking, and more, providing expertise and solutions to address industry-specific challenges. Kyndryl's global network and strategic alliances empower enterprises to adapt to the evolving technology landscape, ensuring their essential systems are reliable and efficient.

📋 Description

• Participate in and facilitate requirements gathering activities such as discovery sessions, interviews, and workshops. • Document and validate requirements, including problem statements, success measures, constraints, assumptions, and risks. • Architect and design enterprise application solutions. • Assess current systems management practices. • Perform service level agreement negotiation and documentation. • Prepare and deliver client-ready presentations (findings, options, recommendations, prototypes, and progress updates). • Translate business needs into AI use cases, including value hypotheses, feasibility considerations, and ethical/risk considerations. • Support stakeholder alignment by summarizing trade-offs (e.g., accuracy vs. explainability; cost vs. performance; build vs. buy). • Contribute to project artifacts such as meeting notes, decision logs, RAID logs, and statements of work content (as directed). • Assist with solution design activities including: data understanding and readiness assessment (high level), feature / signal brainstorming, evaluation approach definition (metrics, baselines, acceptance criteria), model/service integration considerations (APIs, workflows, monitoring needs). • Support the creation of prototypes / proofs of concept using approved tools and patterns (e.g., prompt prototypes, small ML experiments, retrieval approaches). • Help define and execute experiments: test plans, evaluation datasets (where provided/approved), and results summaries. • Contribute to technical documentation (solution overview, model cards/AI notes, evaluation summaries, runbooks) at an appropriate level for the audience. • Follow organizational standards for security, privacy, and responsible AI practices as defined by project governance. • Research emerging AI capabilities and patterns relevant to client problems (e.g., agentic workflows, RAG approaches, evaluation techniques). • Run structured experiments to compare approaches and document what worked, what didn’t, and why. • Contribute reusable assets such as templates, checklists, demo scripts, prompt libraries, and “lessons learned” write-ups. • Lead a small pod/workstream of AI specialists (e.g., 1–3 people) on well-scoped tasks by: clarifying goals and deliverables, coordinating work and dependencies, facilitating short stand-ups/check-ins, ensuring quality and timely completion. • Collaborate effectively in a matrixed environment with engineers, analysts, product owners, and client stakeholders. • Seek mentorship and provide peer support through knowledge-sharing and constructive feedback. • Support delivery management by maintaining task status, raising risks early, and helping keep work aligned to timelines. • Contribute to estimates for small tasks (effort ranges, assumptions) and track progress against commitments. • Assist with proposal/support materials (e.g., case study summaries, demo packaging) when needed.

🎯 Requirements

• University Degree in a relevant discipline (e.g., Computer Science, Data Science, Engineering, Information Systems, Statistics, or related) • At least one (1) year of relevant experience in one or more of: AI/ML, analytics, data engineering, or software delivery; consulting, business analysis, or client-facing technology roles; applied research, labs or internship experience with demonstrable outcomes. • Demonstrated ability to present to clients and explain technical concepts in plain language (slides, demos, or written artifacts). • Demonstrated ability to facilitate requirements gathering (workshops/interviews) or strong aptitude with examples. • Foundational knowledge in at least two of the following: ML basics (supervised/unsupervised concepts, evaluation metrics), LLM concepts (prompting, RAG, hallucinations, evaluation), data fundamentals (data quality, schemas, pipelines at a conceptual level), software fundamentals (APIs, version control, testing concepts). • Demonstrated ability to synthesize ideas and use abstract thought to understand new business domains. • Demonstrated ability to lead and positively influence others to achieve results that meet the organization’s strategic objectives. • Basic level project management skills would be considered an asset. • Demonstrated administration and organizational skills. • Excellent oral and written communication skills.

🏖️ Benefits

• Flexible work arrangements • Professional development opportunities