AI Engineer

Location: Noida, Uttar Pradesh, India | Experience: Mid Level (3-5 years)

About the Company

Times Internet is a leading digital products and technology company building platforms that serve millions of users across India and global markets. Our portfolio spans news, entertainment, sports, finance, local discovery, and other high-scale consumer experiences. We combine strong brands, deep audience understanding, technology, and data to create products that are useful, engaging, and commercially meaningful. Our teams operate in an environment where engineering quality, experimentation, and customer impact matter. As an AI Engineer, you will contribute to products that reach large and diverse audiences, helping turn advances in machine learning into dependable capabilities used by real customers. You will work with product, engineering, data, and business stakeholders to identify valuable opportunities, build practical solutions, and improve them through evidence. The role offers the opportunity to solve technically challenging problems while connecting your work to measurable outcomes such as relevance, personalization, efficiency, engagement, and business growth. We value ownership, thoughtful execution, and the ability to move from prototype to production responsibly.

About the Role

Own the design, development, evaluation, and productionization of AI and machine learning systems across Times Internet’s digital products. You will translate product and business problems into reliable technical solutions, using data, experimentation, and sound engineering judgment. The role spans the full lifecycle: understanding the problem, preparing data, selecting and training models, deploying services, monitoring performance, and improving outcomes over time. You will partner closely with product managers, software engineers, data scientists, and operational stakeholders to ensure AI capabilities are useful, scalable, secure, and maintainable. Success means delivering measurable improvements in user experience, content relevance, automation, operational efficiency, or revenue while establishing practices that make future AI development faster and more dependable.

Key Responsibilities

• Own end-to-end delivery of machine learning and AI capabilities, from problem framing and data preparation through deployment, monitoring, iteration, and measurable improvements in product or business outcomes.
• Build and productionize models for relevant use cases such as recommendation, ranking, personalization, classification, forecasting, natural language, or intelligent automation, selecting approaches appropriate to the available data and constraints.
• Develop reliable data and model pipelines with clear validation, versioning, testing, and observability, ensuring systems remain reproducible, maintainable, and resilient as usage and data volumes grow.
• Partner with product, engineering, analytics, and business teams to define success metrics, prioritize high-value opportunities, communicate trade-offs, and convert ambiguous requirements into executable AI solutions.
• Evaluate model quality and product impact through offline analysis, experimentation, A/B testing, and monitoring, using evidence to improve relevance, accuracy, latency, user experience, and operational performance.
• Apply responsible AI and engineering practices, including privacy awareness, bias assessment, security, documentation, and human oversight where appropriate, protecting users and strengthening trust in AI-enabled products.
• Contribute to technical standards, reusable components, code reviews, and knowledge sharing that raise engineering quality and help teams deliver AI solutions consistently across the organization.

Essential Skills & Technologies

• Strong Python programming and practical experience with machine learning libraries such as PyTorch, TensorFlow, scikit-learn, or equivalent frameworks, supported by sound software engineering, testing, and version-control practices.
• Experience building and deploying machine learning systems using data pipelines, APIs, containers, cloud or distributed infrastructure, and monitoring; familiarity with SQL and production debugging is essential.
• Strong understanding of model evaluation, experimentation, statistics, and machine learning fundamentals, with the ability to explain technical decisions and connect model performance to user or business outcomes.

Additional Plus

• Experience with large language models, generative AI, embeddings, retrieval-augmented generation, prompt evaluation, or agentic workflows, including practical attention to quality, latency, cost, and safety.
• Experience in recommendation systems, search, ranking, personalization, content intelligence, natural language processing, or other consumer-facing AI applications operating at meaningful scale.
• Familiarity with MLOps tools and practices such as feature stores, model registries, workflow orchestration, CI/CD, observability, and automated retraining or model governance.

What You'll Bring

• You bring 3–5 years of hands-on experience delivering machine learning or AI systems beyond experimentation, with evidence that your work reached production and created measurable product, customer, operational, or commercial value.
• You bring strong ownership and structured problem-solving, turning unclear objectives into measurable plans, making pragmatic technical choices, and following through from initial hypothesis to stable production impact.
• You bring the ability to work across disciplines, explaining complex concepts clearly to product, engineering, analytics, and business partners while listening carefully to context, constraints, and customer needs.
• You bring disciplined engineering habits, including readable code, testing, documentation, reproducible experimentation, monitoring, and thoughtful handling of data quality, privacy, reliability, and model-risk considerations.

Why Join Us

Times Internet gives you the opportunity to apply AI to products used by large and diverse audiences across digital media, entertainment, sports, finance, and local discovery. Your work will not remain isolated in a research environment; it can shape experiences that customers use every day and influence meaningful product and business decisions. You will solve problems where scale, relevance, speed, and reliability matter, balancing sophisticated modeling with practical delivery. The role offers broad exposure across the AI lifecycle, from understanding user needs and forming hypotheses to building systems, measuring outcomes, and improving them in production. You will collaborate with experienced product, engineering, data, and business teams, giving you the context needed to make high-leverage technical choices. You will also have room to explore emerging approaches, including generative AI, while maintaining the engineering discipline required for dependable systems. If you enjoy combining machine learning depth with product judgment, ownership, and visible impact, this role provides a strong platform to grow your career and help shape the next generation of digital experiences.

What We Offer

• Work on AI-powered products with substantial reach, where your engineering decisions can improve customer experience, engagement, relevance, efficiency, and business performance at meaningful scale.
• Collaborate with cross-functional teams across product, engineering, data, and business domains, gaining broad context and the opportunity to own solutions from concept through production impact.
• Build practical expertise across modern machine learning, generative AI, experimentation, MLOps, and responsible deployment while working on problems that require both technical depth and product judgment.
• Grow in a hybrid working environment that values ownership, learning, clear communication, engineering quality, and measurable outcomes.