Principal Engineer - AI/ML

What you will do:

• Experiment, design, develop and maintain generative AI models (e.g., LLMs, multimodal models), agentic AI architectures, and pipelines with high potential for value and scale.
• Collaborate with other GenAI engineers, data scientists, product managers, and other engineers to ensure successful implementation of generative AI solutions.
• Perform research and testing to develop or customize GenAI algorithms, prompt engineering strategies, and agentic workflows; conduct model training, fine-tuning, and evaluation as needed; integrate, test, tune and monitor the solutions developed.
• Research and evaluate new technologies and tools for generative AI, including Retrieval-Augmented Generation (RAG), prompt orchestration, agentic AI frameworks, model evaluation and safety tools.
• Maintain and update existing generative AI systems, including prompt libraries, agent workflows, and deployed models.
• Troubleshoot and debug GenAI systems, including issues related to model outputs, prompt reliability, hallucinations, and agentic behaviors.
• Work collaboratively with cross-functional partners and stakeholders, identify opportunities for business impact, understand, refine, and prioritize requirements for GenAI models and solutions, drive engineering decisions, and quantify impact.
• Hands-on development, productionization, and operation of GenAI models, agents, and pipelines at scale, including both batch and real-time use cases.
• Work with large scale structured and unstructured data, build and continuously improve cutting edge GenAI models, including LLMs, vision-language models, and agentic systems.
• Provide technical guidance and mentorship to other team members and interns in GenAI best practices, prompting strategies, and agentic design.
• Identify areas of improvement in existing GenAI systems, including prompt engineering, agent reliability, and model deployment.

What you will need:

• B.E/B.Tech/M.Tech/M.E degree in Computer Science or equivalent
• 10-12 years of industry experience in applied AI/GenAI, designing and developing scalable enterprise level solutions
• Experience in architecting and building large, highly scalable systems & software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, agentic workflows, prompt orchestration)
• Strong programming (Python / C++ or equivalent) and data engineering skills
• Deep understanding of Generative AI best practices (e.g., prompt engineering, RAG, agentic design, model fine-tuning, optimization), LLMs, multimodal models, and deep learning basics.
• Experience with these technologies: OpenAI GPT, Llama, Hugging Face Transformers, LangChain, DeepSpeed, Ray, Kubernetes, Spark, Kafka (or equivalent).
• Industry experience building end-to-end GenAI infrastructure and/or building and productionizing Generative AI models, agents, and workflows
• Experience with MLOps/LLMOps practices and tools (e.g., MLflow, Weights & Biases, DVC, SageMaker, Vertex AI)
• Design, implement and integrate the next generation of Generative AI infrastructure to empower other Data Scientists and AI engineers to build GenAI models and agents that make real-time decisions.
• You will collaborate with other engineers and data scientists to create optimal experiences on the Core GenAI platform, including but not limited to: prompt libraries, agentic orchestration, the real-time serving layer, and the offline training system
• Strong collaboration and communication skills, both verbal and written
• Bring a deep empathy for customer needs and insights as well as an intuitive grasp of the business problems we’re trying to solve

Good to have:

• Experience with traditional machine learning and deep learning frameworks and algorithms (e.g., RNNs, CNNs, Transformers, GANs)
• Knowledge of reinforcement learning, transfer learning, and meta-learning concepts
• Hands-on experience with TensorFlow, PyTorch, JAX, Keras
• Familiarity with data labeling platforms, ML model monitoring and evaluation tools
• Experience with MLOps/LLMOps practices and tools (e.g., MLflow, Weights & Biases, DVC, SageMaker, Vertex AI)
• Exposure to model safety, bias detection, explainability, and responsible AI practices
• Experience with cloud platforms (AWS, Azure, GCP) for scalable AI deployments
• Contributions to open source GenAI/ML projects or research publications