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Technical Product Manager

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Hybrid

Technical Product Manager

Innodata India

Technical Product Manager

Skills

Google Cloud PlatformSQL QueriesPythonData ScienceTesting & QANumPypandasPyTorchTensorFlowGitData AnalysisMachine LearningAIAWSNLP / Natural Language ProcessingProduct ManagerAzure

2 months ago

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Important information


Contract type:

Permanent

Location:

Noida, Uttar Pradesh, India

Work mode:

Hybrid

Published on:

21 August 2026

What they need


Job Title: Technical (AI) Product Manager AI/LLM Delivery & Dataset Quality

Function: AI-LLM Project Delivery

Role Type: Individual Contributor – Mid-Level

Location: [Location / Hybrid / Remote]

Reports To: Delivery Unit Head

Works Closely With: Technical Product Managers, ML Engineers, SMEs, Quality Teams, Project Managers, Engineering Teams and AI Researchers

Overview

We are seeking a highly skilled Technical Product Manager to join our AI/LLM Delivery Unit, responsible for designing, developing, and delivering data-driven and AI-powered solutions for enterprise clients. This role combines strong analytical expertise, machine learning experience, and hands-on exposure to Large Language Models (LLMs) to support high-impact client engagements.

The ideal candidate will be both technically strong and client-facing, capable of translating business requirements into scalable AI solutions.

Key Responsibilities

1. AI / Machine Learning Development

  • Design, develop, and deploy machine learning models and data science solutions
  • Work on LLM-based use cases such as prompt engineering, evaluation, fine-tuning, and model optimization
  • Build and optimize pipelines for structured and unstructured data

2. LLM & Generative AI Solutions

  • Develop and evaluate applications using LLMs (e.g., text generation, classification, summarization, entity extraction)
  • Conduct prompt engineering and experimentation to improve model outputs
  • Support model benchmarking, testing, and performance evaluation

3. Data Analysis & Insights

  • Analyze large datasets to extract actionable insights and patterns
  • Perform exploratory data analysis (EDA) and feature engineering
  • Build data visualizations and reports for stakeholders

4. Client Engagement / Delivery

  • Participate in client-facing technical discussions and presentations
  • Translate business requirements into AI/ML solutions and solution architectures
  • Provide consultative insights based on project learnings and industry trends

5. Collaboration & Delivery Excellence

  • Work closely with cross-functional teams (engineering, QA, annotation, delivery leads)
  • Support end-to-end project lifecycle: requirement gathering development deployment monitoring
  • Ensure quality, scalability, and reliability of AI deliverables

Qualifications

Education

  • Master’s degree in data science, Computer Science, Mathematics, Statistics, or related field
  • Master’s degree or PhD preferred, especially for advanced AI/ML roles

Experience

  • 2–6+ years of experience in data science, machine learning, or AI solutions
  • Experience working on AI/LLM or NLP-related projects is highly preferred
  • Exposure to client-facing or delivery environments is a strong advantage

Technical Skills

  • Strong proficiency in Python (NumPy, Pandas, Scikit-learn)
  • Experience with ML frameworks (TensorFlow, PyTorch)
  • Hands-on experience with LLMs / NLP tools (e.g., Hugging Face, OpenAI APIs, embeddings, RAG frameworks)
  • Support evaluation and benchmarking methodologies used to determine dataset quality and model performance.
  • Work with Subject Matter Experts, Quality teams, and Technical Product Managers to investigate potential data or Ground Truth issues.
  • Solid understanding of:
  • Machine learning algorithms
  • Data preprocessing and feature engineering
  • Model evaluation and performance metrics
  • Experience with:
  • SQL and large-scale datasets
  • Cloud platforms (AWS, Azure, or GCP)
  • Version control (Git)
  • Strong hands-on programming capability in Python.

Core Competencies

  • Strong analytical and problem-solving skills
  • Excellent communication skills (written and verbal)
  • Ability to explain complex technical concepts to non-technical stakeholders
  • High attention to detail and quality
  • Collaborative mindset and adaptability in fast-paced environments

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