Senior AI ML Engineer

Delta Tech Hub; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Delta Tech Hub in Bengaluru seeks an AI/ML engineer to translate opportunities and ambiguous requirements into well-defined AI/ML problems. Design and develop production-grade AI services, APIs, microservices, and integrations using sound software engineering principles.

Build solutions using generative AI, natural language processing, retrieval, classification, prediction, recommendation, or optimisation. Develop AI workflows with prompting, tool use, agent orchestration, search,

Responsibilities

• Work with business and technology stakeholders to translate opportunities and ambiguous requirements into well-defined AI or machine-learning problems.
• Design and develop production-grade AI services, APIs, microservices, and integrations using sound software engineering principles.
• Build solutions using appropriate techniquessuch as generative AI, natural language processing, retrieval, classification, prediction, recommendation, oroptimisation.
• Develop AI application workflows that may include prompting, structured outputs, tool use, agent orchestration, search, retrieval-augmented generation, and conversational context.
• Create andmaintain data ingestion, preparation, indexing, retrieval, and model-serving pipelines whererequired.
• Define evaluation approaches using representative datasets, automated tests, quality metrics, human review, and business outcomes.
• Implement appropriate safety, security, privacy, and responsible-AI controls, including protections for sensitive data and unreliable or harmful modelbehaviour.
• Deploy, monitor, and support AI solutions in cloud environments, balancing quality, reliability, latency, scalability, and cost.
• Investigate production issues using logs, traces, metrics, evaluation results, and user feedback, and drive improvements through the full product lifecycle.
• Integrate AI capabilities with enterprise systems, external services, and existing technology platforms.
• Contribute to architecture, technical documentation, code reviews, testing standards, CI/CD practices, and reusable engineering patterns.
• Clearly communicate assumptions, limitations, risks, uncertainty, and technical trade-offs to technical and non-technical audiences.
• Mentor engineers and contribute to continuous learning across the wider AI and engineering community.
• Develop an understanding of the airline and travel domain and how technology supports customers, colleagues, and operations.

Job description

• Work with business and technology stakeholders to translate opportunities and ambiguous requirements into well-defined AI or machine-learning problems.
• Design and develop production-grade AI services, APIs, microservices, and integrations using sound software engineering principles.
• Build solutions using appropriate techniquessuch as generative AI, natural language processing, retrieval, classification, prediction, recommendation, oroptimisation.
• Develop AI application workflows that may include prompting, structured outputs, tool use, agent orchestration, search, retrieval-augmented generation, and conversational context.
• Create andmaintain data ingestion, preparation, indexing, retrieval, and model-serving pipelines whererequired.
• Define evaluation approaches using representative datasets, automated tests, quality metrics, human review, and business outcomes.
• Implement appropriate safety, security, privacy, and responsible-AI controls, including protections for sensitive data and unreliable or harmful modelbehaviour.
• Deploy, monitor, and support AI solutions in cloud environments, balancing quality, reliability, latency, scalability, and cost.
• Investigate production issues using logs, traces, metrics, evaluation results, and user feedback, and drive improvements through the full product lifecycle.
• Integrate AI capabilities with enterprise systems, external services, and existing technology platforms.
• Contribute to architecture, technical documentation, code reviews, testing standards, CI/CD practices, and reusable engineering patterns.
• Clearly communicate assumptions, limitations, risks, uncertainty, and technical trade-offs to technical and non-technical audiences.
• Mentor engineers and contribute to continuous learning across the wider AI and engineering community.
• Develop an understanding of the airline and travel domain and how technology supports customers, colleagues, and operations.