Principal Engineer- AI/ML

Designation- Principal Engineer- AI/ML

Exp- 10-15 yrs

Location- Hyderabad (Hybrid mode)

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

JD

• -10-15 years of industry experience in applied AI/GenA I, designing and developing scalable enterprise level solution
• sExperience 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 skill
• sDeep 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 workflow
• sExperience 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 syste
• mStrong collaboration and communication skills, both verbal and writte
• nBring 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 hav

• e:Experience with traditional machine learning and deep learning frameworks and algorithms (e.g., RNNs, CNNs, Transformers, GAN
• s)Knowledge of reinforcement learning, transfer learning, and meta-learning concep
• tsHands-on experience with TensorFlow, PyTorch, JAX, Ker
• asFamiliarity with data labeling platforms, ML model monitoring and evaluation too
• lsExperience with MLOps/LLMOps practices and tools (e.g., MLflow, Weights & Biases, DVC, SageMaker, Vertex A
• I)Exposure to model safety, bias detection, explainability, and responsible AI practic
• esExperience with cloud platforms (AWS, Azure, GCP) for scalable AI deploymen
• tsContributions to open source GenAI/ML projects or research publicatio

ns