Description :

- B.E/B.Tech/M.Tech/M.E degree in Computer Science or equivalent

- 10-15 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 were 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