AI/ML Delivery Manager

Innodata Inc.; India; On-site; INR 2,800,000 - 4,200,000; Full time

Job summary

I nnodata Inc. is seeking a Technical Delivery Manager in India to own end-to-end AI/ML program management, spanning model development, training, LLMs, data pipelines, and evaluation.

The role acts as SPOC for clients and coordinates across CXOs, practice heads, engineering, hiring, and operations to keep delivery aligned with client expectations and business goals.

Qualifications

• 8+ years of experience in technical program/delivery management.
• 2–3 years managing AI/ML, data science, or data engineering programs.
• Graduate in engineering/Technology/Data Science from Tier 1 institute.
• Demonstrated experience as primary client-facing SPOC for CXO-level stakeholders.
• Strong knowledge of ML/AI project lifecycle and MLOps concepts.

Responsibilities

• Own the end-to-end delivery roadmap for AI/ML programs, including model development, training, LLM implementations, and evaluation workstreams.
• Translate client requirements into structured delivery plans, milestones, and success metrics.
• Track scope, timelines, budgets, and resourcing; flag slippages and drive corrective action.
• Establish governance cadences across all active engagements.

Skills

Technical program management

AI/ML project delivery

Executive communication

Cross-functional leadership

Jira/Confluence/Asana

Education

Engineering/Technology/Data Science degree

Tools

Jira

Confluence

MS Project

Asana

Job description

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.

Key Responsibilities

1. 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; proactively flag slippages and drive corrective action.
• Establish and maintain governance cadences (status reviews, steering committee updates, sprint/iteration reviews) across all active engagements.

2. Client & Stakeholder Management

• Serve as the single SPOC for clients — managing relationships from day-to-day points of contact through to CXO-level stakeholders.
• Run regular client check-ins, business reviews, and escalation calls; 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.

3. Cross-Functional Orchestration

• 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.

4. Risk, Quality & Proactive Issue Management

• Proactively identify delivery risks (technical, resourcing, scope, timeline) early and drive mitigation plans before they escalat.
• Anticipate client concerns based on program signals (e.g., 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.

6. Reporting & Governance

• Maintain accurate, real-time visibility into program health for internal leadership and client stakeholders.
• 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.

Required Skills & Experience

• 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.
• Graduate in engineering/Technology/Data Science from Tier 1 institute.
• Demonstrated experience being the primary client-facing SPOC for enterprise or CXO-level stakeholders.
• 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).
• Excellent executive communication and presentation skills; comfortable translating technical detail into business impact for CXO audiences.
• Experience with program/delivery tooling (e.g., Jira, Asana, MS Project, Confluence) and reporting/dashboarding tools.

Preferred Qualifications

• 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).
• Bachelor’s degree in engineering, Computer Science, or related field; MBA or equivalent is a plus.

Key Attributes

• Orchestrator mindset — comfortable being the hub that connects client, engineering, hiring, and operations without owning every technical decision directly.
• Proactive, not reactive — anticipates risks, resourcing gaps, and client concerns before they surface as problems.
• Trusted communicator — equally credible in a technical review with engineers and a business review with a CXO.
• Ownership-driven — treats client outcomes and program health as personally accountable, end-to-end.