NTT DATA is hiring for an FRESHER entry level AI Engineer role in India

Position:

• AI Engineer

Company:

• NTT DATA 

Location:

• Bengaluru East, Karnataka, India

Job type:

• Full time

Job mode:

• Hybrid

Job requisition id:

• 343647

Years of experience:

• Open to fresh graduates and early professionals up to three years

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Company description

• NTT DATA North America operates as part of a large global group that focuses on business and technology services for clients across many industries and regions.

• The company supports organizations with consulting, digital solutions, data and artificial intelligence practices, and managed services for applications, infrastructure, and connectivity.

• A large presence across countries allows access to diverse talent, global delivery capability, and learning exposure for employees.

• The company works closely with clients to improve business performance, create better customer experiences, and adopt modern technologies with confidence and responsibility.

• It partners with established firms and emerging companies to bring innovation into practical use for clients, helping them address real business needs through solutions built on data, cloud adoption, and artificial intelligence.

• Employees learn through exposure to different sectors such as financial services, healthcare, public sector, manufacturing, and others, which widens practical knowledge and problem solving ability.

• The organization promotes inclusion, collaboration, and adaptability, encouraging employees to contribute ideas and grow through continuous learning and real project work.

• Investment in research and development supports the introduction of new methods and tools that improve delivery quality, security, and sustainability outcomes for clients and society.

• Local hiring near client sites helps ensure close coordination, quicker support, and strong relationships with customers, while remote and flexible options may be available based on project needs.

• The company follows strong ethics in recruitment and employment practices, with emphasis on equal opportunity and a safe work environment free from discrimination.

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Profile overview

• The role focuses on building and improving solutions that make use of artificial intelligence, especially large language models and agentic systems that perform tasks through reasoning and tool use.

• The AI Engineer will design components that connect these models with applications, data sources, and external services using application programming interfaces.

• Work includes creating orchestration layers that manage prompts, context, model selection, and result handling so that intelligent systems can perform multi step tasks in real business situations.

• The position involves performance evaluation and testing of models to understand accuracy, reliability, latency, and robustness under real usage conditions.

• Python will be a primary programming language, and the engineer will use it to build pipelines, automation, evaluation scripts, and integrations with third party services.

• The engineer will collaborate with data scientists, software engineers, domain experts, and client stakeholders to convert business requirements into working AI solutions.

• Documentation, version control, and code quality practices form an important part of daily work, supporting maintainability and knowledge sharing across teams.

• The role offers exposure to modern AI tools, frameworks, and experimentation practices that help improve both technical skill and problem solving confidence.

• The engineer contributes to innovation initiatives where new ideas are converted into prototypes and then into production grade systems for clients.

• The work environment encourages initiative, responsibility, and learning, making it suitable for candidates who are curious, motivated, and willing to build strong foundations in applied artificial intelligence.

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Qualifications

• Strong programming ability in Python, with comfort in writing clean, readable, and modular code.

• Understanding of large language models, generative AI concepts, prompt design, and basic principles behind model behavior.

• Exposure to building integrations with application programming interfaces to connect AI models with external tools, databases, and applications.

• Ability to design orchestration layers that manage multiple steps, tool calling, context passing, and interaction between components in AI systems.

• Familiarity with basic data structures, algorithms, and software development practices such as testing, debugging, and version control.

• Exposure to machine learning concepts such as supervised and unsupervised learning, model evaluation metrics, and dataset handling.

• Comfort working in collaborative environments with cross functional teams, communicating technical ideas in simple language when needed.

• Ability to read technical documentation, learn new frameworks quickly, and apply them to practical tasks without frequent supervision.

• Academic background in computer science, information technology, data science, or related fields is preferred, although equivalent practical skills are valued.

• Positive attitude toward continuous learning, openness to feedback, and willingness to improve both coding skills and business understanding over time.

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Additional info

• The position is based in India with assignment at Bengaluru East, and may involve coordination with teams in other countries.

• Work arrangements can include office presence, remote contribution, or a mix of both, as guided by client needs and project stages.

• The organization emphasizes ethical practices in hiring and will not request payments or banking information during recruitment.

• Candidates should remain alert to genuine company communication channels and ignore any suspicious messages not originating from official addresses.

• The company supports equal opportunity employment, providing a respectful workplace for individuals from a wide variety of backgrounds.

• Employees may participate in meetings, knowledge sessions, and events that promote growth, collaboration, and business understanding.

• The role may evolve in responsibilities as projects progress, giving exposure to solution design, implementation, testing, deployment, and monitoring.

• Candidates joining at fresher or early career level will receive guidance from experienced team members to help them gain confidence in real projects.

• Performance in this role can open pathways into advanced AI engineering, solution architecture, project leadership, and domain specialization in the future.

• The organization encourages safe work practices, clear communication, and responsibility toward clients and colleagues.

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Key responsibilities for the AI Engineer role

• Build, test, and refine AI components that support conversational systems, automation tools, and intelligent applications for clients.

• Create orchestration layers that coordinate large language models, tools, data sources, and external services through application programming interfaces.

• Design prompt structures, context windows, and control logic so that AI agents can perform multi step tasks with clarity and stability.

• Conduct performance evaluation of AI models, including accuracy checking, error pattern study, and improvement suggestions.

• Prepare datasets or scenarios for testing model responses under different real world conditions and edge cases.

• Work with security and compliance guidelines when handling client data, ensuring responsible use and protection of information.

• Contribute to end to end solution building from idea to prototype to production ready system in collaboration with senior engineers.

• Participate in code reviews, share feedback respectfully, and adopt suggested improvements to enhance overall solution quality.

• Document processes, architecture choices, experiment findings, and implementation steps for future reference by the team.

• Support integration of AI components into existing enterprise systems, web applications, chat interfaces, or analytical platforms.

• Understand client requirements by interacting with project leads and translate them into technical tasks and deliverables.

• Troubleshoot production issues, analyze logs and behavior patterns, and suggest stable fixes without compromising user experience.

• Explore new AI tools and libraries, test feasibility, and provide practical insights on their usability in client environments.

• Help in creating internal utilities that speed up development, testing, and monitoring activities for AI solutions.

• Participate in knowledge sharing sessions, internal workshops, and learning circles to strengthen understanding of artificial intelligence in business use cases.

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Technical focus areas

• Large language model orchestration, including design of controllers that decide when and how models are invoked for specific tasks.

• Agentic AI design where systems can reason step by step, call tools, and adapt based on intermediate results to reach final outcomes.

• Application programming interface integration with third party tools such as databases, enterprise platforms, productivity suites, and cloud services.

• Prompt engineering with attention to clarity, constraints, context, and formatting for consistent model behavior across repeated runs.

• Python based development using libraries that simplify data processing, model interaction, evaluation pipelines, and automation tasks.

• Experiment tracking and result comparison to determine which approach performs better for a given client scenario.

• Error analysis to understand hallucinations, failure modes, bias issues, and robustness challenges in generative AI systems.

• Token management, context window planning, and cost awareness when working with external model providers.

• Logging, monitoring, and observability practices that help detect anomalies and maintain reliability in live systems.

• Use of containerization and deployment practices in collaboration with DevOps teams for moving solutions from development to production environments.

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Collaboration and stakeholder engagement

• Work closely with data scientists who design algorithms and models, ensuring alignment between research ideas and implementation realities.

• Coordinate with software engineers who build front end and back end systems, making sure AI components integrate smoothly with other services.

• Interact with project managers to understand milestones, priorities, deadlines, and client expectations clearly.

• Participate in discussions with client representatives, when required, to explain solution behavior and obtain clarification on requirements.

• Provide timely updates on progress, challenges, and risks so that teams can plan appropriately and avoid delays.

• Contribute to proposal building or solution demonstrations for potential projects by preparing prototypes and sample outputs.

• Support knowledge transfer activities so that client technical teams can understand how to operate and maintain deployed systems.

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Tools and technologies exposure

• Python as the primary language for development, scripting, experimentation, and integration tasks.

• Version control platforms for managing code, branches, and collaborative updates with team members.

• Libraries for working with large language models, prompt templates, and agent frameworks.

• Application programming interface clients and tools for connecting with external services and enterprise platforms.

• Data handling libraries for cleaning, transforming, and analyzing text data or structured records used in AI processing.

• Test automation frameworks for unit tests, integration tests, and regression checks of AI components.

• Documentation tools for preparing technical notes, diagrams, and solution descriptions.

• Monitoring dashboards and logging platforms used in production environments to keep track of performance and reliability.

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Model development and evaluation

• Design test cases that reflect real business interactions such as customer support questions, report generation tasks, or analytical queries.

• Measure response quality through relevance, correctness, completeness, and clarity for the user.

• Compare different models or configurations to select the one that best meets client goals in terms of cost, speed, and reliability.

• Apply safety checks and guardrails to reduce harmful or inappropriate outputs based on use case requirements.

• Build scripts that automate evaluation runs over many prompts to generate quantitative metrics and qualitative insights.

• Document evaluation results with observations and recommendations for next steps in improvement.

• Collaborate with domain experts to ensure outputs align with regulatory guidelines, business tone, and operational constraints.

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Quality, security, and governance

• Follow coding standards, documentation practices, and peer review processes to maintain solution quality.

• Handle data responsibly, respecting privacy, confidentiality, and client specific compliance requirements.

• Participate in audits or internal reviews related to project delivery, security practices, or process maturity.

• Report risks, vulnerabilities, or unusual behavior observed in AI models so that mitigation plans can be implemented.

• Support implementation of controls that restrict sensitive content generation or unintended use of AI systems.

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Learning and career growth

• Access to training resources, internal communities, and mentorship opportunities that help enhance technical and professional skills.

• Exposure to projects from different industries provides a broad view of how artificial intelligence supports real business outcomes.

• Opportunity to contribute ideas for innovation programs, hackathons, or proof of concept initiatives.

• Performance in this role can open pathways toward senior AI engineer positions, solution design roles, or technical leadership tracks.

• Early career professionals gain confidence by working on real client projects with guidance from experienced mentors.

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Work environment and culture

• Collaborative teams that encourage sharing of knowledge and constructive feedback.

• Respect for diversity and inclusion across geography, background, education, and work styles.

• Flexible arrangements guided by business needs with attention to employee well being and productivity.

• Emphasis on ethics, transparency, and fairness in all employment practices.

Please click here to apply.