Senior Data Scientist

Role description

Senior AI Engineer Generative AI (GenAI) + Cloud (AWS/GCP/Azure)

We are seeking a Senior AI Engineer with deep expertise in Generative AI (GenAI) and hands-on experience deploying solutions on cloud platforms (AWS, GCP, or Azure). This role focuses on building scalable, production-ready GenAI systems, integrating LLMs into enterprise applications, and optimizing performance in cloud-native environments. You will collaborate with data scientists, product managers, and engineering teams to operationalize cutting-edge AI/ML innovations.

Key Responsibilities

• Design and deploy GenAI solutions leveraging LLMs (e.g., GPT, LLaMA, Claude, PaLM) for enterprise use cases such as summarization, content generation, semantic search, and conversational AI.
• Implement RAG pipelines using frameworks like LangChain, LlamaIndex, and vector databases (FAISS, Pinecone, Weaviate).
• Architect scalable AI systems on cloud platforms (AWS Sagemaker, GCP Vertex AI, or Azure Machine Learning) using services such as BigQuery, DynamoDB, Kubernetes (EKS/GKE/AKS), and serverless functions.
• Collaborate with cross-functional teams to productionize models, ensuring robust CI/CD pipelines, monitoring, and automated retraining.
• Optimize model serving for latency, throughput, and cost efficiency in cloud environments.
• Integrate GenAI APIs (OpenAI, Anthropic, Google Cloud GenAI, Azure OpenAI) into enterprise applications and co-pilot solutions.
• Establish MLOps best practices for observability, reproducibility, and secure deployment of GenAI workloads.
• Stay updated on GenAI research, tools, and cloud advancements to continuously improve system capabilities.

Required Skills and Experience

• 5+ years of experience in AI/ML engineering, with 2+ years in Generative AI or LLM-based systems.
• Strong proficiency in Python and experience with ML frameworks (PyTorch, TensorFlow, Hugging Face Transformers).
• Hands-on expertise with cloud services (AWS Sagemaker, GCP Vertex AI, or Azure ML).
• Experience with LangChain, LlamaIndex, and vector databases for RAG pipelines.
• Solid understanding of NLP, embeddings, and generative modeling techniques.
• Proven track record of deploying ML/AI models in production cloud environments.
• Familiarity with CI/CD, containerization (Docker, Kubernetes), and MLOps practices.

Preferred Qualifications

• Master’s or PhD in Computer Science, Machine Learning, or related field.
• Experience with cloud-native GenAI APIs (Azure OpenAI, Google Gemini, AWS Bedrock).
• Background in building chatbots, copilots, or enterprise-grade GenAI applications.
• Contributions to open-source projects in GenAI or cloud ML ecosystems.
• Knowledge of security, compliance, and governance for AI workloads in cloud environments.

Skills

Python, GenAI, Agentic AI, LLM, Langraph, RAG, Azure, AWS, GCP