Remote — open to applicants worldwide.
We are hiring a Senior Project Manager to lead the delivery of complex initiatives with a strong emphasis on requirements analysis, stakeholder management, and process modelling. This role sits at the center of business and technical coordination, translating requirements into structured delivery plans and guiding work across multiple stakeholders. The position has meaningful impact on how projects are defined, governed, and executed.
• Requirements analysis — target L8 (Adv), must-have • Stakeholder management — target L8 (Adv), must-have • Process modelling — target L8 (Adv), must-have • Data analysis — target L8 (Adv), nice-to-have • SQL — target L8 (Adv), nice-to-have • AI/ML — target L10 (Exp), nice-to-have • agile — target L6 (Prof), nice-to-have • microsoft azure — target L9 (Exp), nice-to-have • data bricks — target L8 (Adv), nice-to-have • AWS — target L9 (Exp), nice-to-have • azure — target L8 (Adv), nice-to-have • GCP — target L8 (Adv), nice-to-have • MLOPs — target L9 (Exp), nice-to-have • Working AI leverage — observed and scored, never assumed • 8–20 years of relevant experience From the hiring team: Job Overview We are seeking an experienced and results-driven Project Manager – AI, Data & Machine Learning to lead the planning, execution, and successful delivery of complex AI, Data Engineering, Analytics, and Machine Learning initiatives. The ideal candidate will have a strong background in technical project management, with hands-on experience managing enterprise-scale AI/ML projects, data modernization programs, cloud-based data platforms, and cross-functional technical teams. This role requires a combination of strong project delivery capabilities, technical understanding of AI/ML ecosystems, stakeholder management, and Agile execution methodologies. Key Responsibilities 1. AI, Data & ML Project Management • Lead end-to-end delivery of AI, Machine Learning, Data Engineering, and Advanced Analytics projects. • Manage project scope, schedules, budgets, resources, dependencies, risks, and deliverables. • Oversee AI/ML initiatives from discovery and proof of concept (POC) through development, deployment, and production support. • Coordinate multiple workstreams involving Data Scientists, ML Engineers, Data Engineers, Cloud Architects, and Business Analysts. • Ensure timely delivery of projects while maintaining quality, governance, and business alignment. 2. Technical Project Delivery • Manage projects involving Machine Learning, Generative AI, Large Language Models (LLMs), and enterprise AI adoption. • Coordinate data ingestion, ETL/ELT pipelines, data integration, data lakes, data warehouses, and cloud data platform initiatives. • Oversee ML model development lifecycles, including data preparation, model training, validation, deployment, monitoring, and MLOps. • Work with technical teams utilizing platforms such as Azure AI, AWS SageMaker, Google Vertex AI, Databricks, Snowflake, and related technologies. • Understand AI solution architecture, APIs, cloud infrastructure, and integration with enterprise applications. • Identify technical dependencies, integration challenges, and delivery risks. 3. Agile Delivery & Governance • Drive Agile/Scrum, Waterfall, or Hybrid project delivery methodologies. • Facilitate sprint planning, backlog prioritization, daily stand-ups, sprint reviews, and retrospectives. • Establish project governance, reporting frameworks, and delivery KPIs. • Track milestones, project performance, resource utilization, and budget adherence. • Maintain project documentation, RAID logs, delivery dashboards, and executive status reports. 4. Stakeholder & Vendor Management • Act as the primary point of contact between business stakeholders, technology teams, external vendors, and senior leadership. • Translate business requirements into actionable technical delivery plans. • Manage stakeholder expectations and communicate project progress, risks, and dependencies. • Coordinate third-party technology providers and implementation partners. • Ensure alignment between technical outcomes and measurable business value. 5. AI Governance, Risk & Compliance • Coordinate AI governance, data privacy, security, and regulatory compliance requirements. • Support responsible AI practices, model validation, data quality, and risk mitigation. • Ensure AI/ML solutions meet enterprise security, scalability, reliability, and performance standards. • Collaborate with architecture, cybersecurity, compliance, and data governance teams. Required Qualifications & Experience • 8–12+ years of overall IT project management experience, including at least 3 years managing AI, Data, Analytics, or Machine Learning initiatives. • Proven experience delivering enterprise-scale data platforms, AI/ML solutions, or cloud transformation projects. • Strong understanding of the AI/ML development lifecycle, data engineering, data architecture, and MLOps. • Experience managing multidisciplinary technical teams in distributed or remote environments. • Working knowledge of cloud platforms such as Microsoft Azure, AWS, or Google Cloud. • Familiarity with Databricks, Snowflake, Azure Data Factory, Apache Spark, or similar data technologies. • Experience with Agile/Scrum delivery and tools such as Jira, Azure DevOps, MS Project, and Confluence. • Strong experience in project budgeting, forecasting, risk management, vendor coordination, and executive reporting. • Excellent communication, leadership, problem-solving, and stakeholder management skills. • Demonstrated ability to manage multiple priorities in a fast-paced enterprise environment. Preferred Qualifications • PMP, PMI-ACP, Certified Scrum Master (CSM), or SAFe certification. • Experience managing Generative AI, LLM, RAG, or AI automation initiatives. • Familiarity with Python, SQL, MLflow, Kubernetes, CI/CD, and MLOps concepts. • Experience with enterprise data modernization, cloud migration, or AI transformation programs. • Knowledge of AI governance frameworks, data privacy, and responsible AI practices. • Prior experience working with North American enterprise clients. Key Success Indicators • On-time and within-budget delivery of AI, Data, and ML projects. • Successful transition of AI/ML solutions from POC to production. • Effective coordination between business, data, engineering, and AI teams. • Proactive management of risks, dependencies, and stakeholder expectations. • Demonstrated delivery of measurable business outcomes.
Lead project delivery by defining requirements, aligning stakeholders, and mapping processes that support effective execution. Translate business needs into actionable plans, coordinate delivery activities across workstreams, and maintain clarity on scope, dependencies, and progress. Support projects that may involve data analysis, SQL, AI/ML, cloud platforms, and MLOPs, while applying structured project management and delivery practices. Contribute to delivery approaches that benefit from agile ways of working where relevant.
Techies4tech.ai is an organization of unspecified industry and size.
This role offers the opportunity to lead high-impact project delivery work where strong requirements analysis, stakeholder management, and process modelling are central to success. It also provides scope to contribute to projects connected to data, AI/ML, cloud, and MLOPs environments, with room to apply preferred technical and delivery experience where relevant.
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