Head of Technical Delivery Management

Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.
We are looking for a Technical Delivery Manager (TDM) to own end-to-end program management for our AI/ML engagements — spanning ML model development, training and fine-tuning, LLM-based solutions, data pipelines, model evaluation, and broader AI/ML workflows.
This role is the single point of contact (SPOC) for clients and internally coordinates across CXO’s, practice heads, engineering, hiring, operations teams. The TDM acts as the orchestrator of the entire delivery engine, ensuring that what clients expect and what gets delivered stay tightly aligned at every stage, while proactively identifying and resolving risks before they become issues.
Program & Delivery Management:

Own the end-to-end delivery roadmap for AI/ML programs, including ML model development, training/fine-tuning, LLM implementations, and model evaluation workstreams.
Translate client requirements and business goals into structured delivery plans, milestones, and success metrics.
Track scope, timelines, budgets, and resourcing across multiple concurrent AI/ML projects; Client & Stakeholder Management:

Serve as the single SPOC for clients — managing relationships from day-to-day points of contact through to CXO-level stakeholders.
present delivery status, risks, and outcomes.
Ensure client expectations are clearly captured, documented, and continuously validated against what is actually being built and delivered — closing any gaps before they surface as dissatisfaction.
Build trusted advisor relationships that support account growth and renewal.
Act as the connective tissue between practice heads, engineering teams, hiring/talent acquisition, and operations, ensuring everyone is working off the same delivery plan and priorities.
Coordinate staffing and hiring pipelines with TA/HR to ensure the right talent is available in time for project ramp-up.
Work with practice/technical leads to validate solution approaches, technical feasibility, and effort estimates before commitments are made to clients.
Partner with operations on resourcing, utilization, invoicing/billing milestones, and contractual compliance.
Risk, Quality & Proactive Issue Management:

Proactively identify delivery risks (technical, resourcing, scope, timeline) early and drive mitigation plans before they escalate.
slipping milestones, performance issues, resourcing gaps) and act ahead of formal escalation.
Own issue/escalation management end-to-end — coordinating the right internal teams to resolve problems quickly and communicating transparently with clients throughout.
Drive continuous improvement in delivery processes, templates, and playbooks based on lessons learned across engagements.
Prepare and present executive-level dashboards and reviews to both client and internal leadership.
Ensure contractual SLAs, deliverable timelines, and commercial commitments are tracked and met.
Bachelor’s degree in engineering, Computer Science, or related field; MBA or equivalent is a plus

• Prior experience in an IT services, consulting, or AI/ML solutions provider environment (client-delivery model, not just internal product teams)
• Exposure to LLM/GenAI project delivery specifically (RAG pipelines, fine-tuning, agentic workflows, evaluation frameworks)
• 8+ years of experience in technical program/delivery management, with at least 2–3 years specifically managing AI/ML, data science, or data engineering programs.
• Strong working knowledge of the ML/AI project lifecycle: model training/fine-tuning, LLM-based solution delivery, model evaluation, and MLOps concepts.
• Proven ability to manage multiple concurrent, cross-functional programs in a matrixed environment (engineering, practice/technical teams, hiring, operations).
• Experience with program/delivery tooling (e.g., Jira, Asana, MS Project, Confluence) and reporting/dashboarding tools.

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