Applied AI Engineer

Join Microsoft's Customer Service Applications Team as an Applied AI Engineer in Bengaluru / Bangalore, India. This role is pivotal in advancing Microsoft's mission to empower individuals and organizations globally by integrating cutting-edge AI into products and services with a focus on inclusivity and ethical impact.

You will collaborate with product, research, and engineering teams to bring innovative AI solutions to life, applying expertise in machine learning, data science, and AI to solve complex challenges. Your contributions will directly shape product direction and customer experience. The team's vision is to build an open architecture platform enabling users to summon tailored AI agents for real-world outcomes. This role demands a blend of AI knowledge, applied science expertise, a growth mindset, and customer empathy to shape the future of AI agents.

Key Responsibilities:

Bringing the State of the Art to Products

• Foster collaborative relationships with product and business groups to deliver AI-driven impact.
• Research and implement state-of-the-art techniques including foundation models, prompt engineering, RAG, graphs, multi-agent architectures, and classical machine learning.
• Utilize strong software engineering skills in C# and Python to design, develop, and optimize scalable, reliable, and maintainable AI-driven systems.
• Fine-tune foundation models with domain-specific datasets.
• Evaluate model behavior for relevance, bias, hallucination, and response quality through offline evaluations, shadow experiments, online experiments, and ROI analysis.
• Develop rapid AI solution prototypes, contribute to their production deployment, debug production code, and support MLOps/AIOps.
• Contribute to academic papers, patents, and conference presentations.
• Translate research into production-ready solutions and measure their impact via A/B testing and telemetry that addresses customer needs.
• Use data to identify gaps in AI quality, uncover insights, and implement Proofs of Concepts (PoCs).

Leveraging Research in Real-World Problems

• Demonstrate deep expertise in AI subfields such as deep learning, Generative AI, NLP, and multi-modal models to translate cutting-edge research into practical, real-world solutions that drive product innovation and business impact.
• Share insights on industry trends and applied technologies with engineering and product teams.
• Formulate strategic plans that integrate state-of-the-art research to meet business goals.

Documentation

• Maintain clear documentation of experiments, results, and methodologies.
• Share findings through internal forums, newsletters, and demos to promote innovation and knowledge sharing.

Ethics, Privacy, and Security

• Apply a deep understanding of fairness and bias in AI by proactively identifying and mitigating ethical and security risks, including Cross-Prompt Injection Attacks (XPIA), unfairness, bias, and privacy concerns, to ensure equitable and responsible outcomes.
• Ensure responsible AI practices throughout the development lifecycle, from data collection to deployment and monitoring.
• Contribute to internal ethics and privacy policies and ensure responsible AI practices throughout the AI development cycle.

Specialty Responsibilities

• Design, develop, and integrate generative AI solutions using foundation models and other advanced techniques.
• Possess a deep understanding of small and large language model architecture, deep learning, fine-tuning techniques, multi-agent architectures, classical ML, and optimization techniques to adapt out-of-the-box solutions to specific business problems.
• Prepare and analyze data for machine learning, identify optimal features, and address data gaps.
• Develop, train, and evaluate machine learning models and algorithms to solve complex business problems using modern frameworks, state-of-the-art models, open-source libraries, statistical tools, and rigorous metrics.
• Address scalability and performance issues using large-scale computing frameworks.
• Monitor model behavior, guide product monitoring and alerting, and adapt to changes in data streams.

Qualifications:

Required Qualifications

• Bachelor's degree in Computer Science, Statistics, Electrical/Computer Engineering, Physics, Mathematics, or a related field, AND 2+ years of experience in AI/ML, predictive analytics, or research.
• OR Master's degree AND 2+ years of experience.
• OR equivalent experience.
• 1+ years of experience with generative AI OR LLM/ML algorithms.
• Strong software engineering skills, including hands-on development experience in C# and Python for building scalable, high-performance, and production-ready systems.

Other Requirements

• Ability to meet Microsoft customer and/or government security screening requirements is necessary for this role. This includes specialized security screenings like the Microsoft Cloud Background Check, required upon hire/transfer and every two years thereafter.

Preferred Qualifications

• Experience with MLOps workflows, including CI/CD, monitoring, and retraining pipelines.
• Familiarity with modern LLMOps frameworks such as LangChain and PromptFlow.
• Experience developing and deploying live production systems with C#, Java, React/Angular, or TypeScript.
• Experience with the design and implementation of enterprise-scale services.
• 1+ years of experience publishing in peer-reviewed venues or filing patents.
• Experience presenting at conferences or industry events.
• 1+ years of experience conducting research in academic or industry settings.
• 1+ years of experience working with Generative AI models and ML stacks.
• Experience across the product lifecycle from ideation to shipping.