Senior Generative AI Engineer - Vice President

• Develop & Prototype: Design, build, and iterate on prototypes for Generative and Agentic AI applications with speed and agility, demonstrating the art of the possible.
• Implement AI Models: Implement, train, and fine-tune a variety of machine learning and deep learning models to solve complex business problems.
• Build Robust Systems: Develop and maintain clean, efficient, and scalable code for AI/ML systems, with a focus on production-level quality.
• Manage Data Pipelines: Engineer and manage sophisticated data handling and preprocessing pipelines to ensure high-quality data for training and inference.
• Deploy & Operate: Utilize MLOps best practices to deploy AI applications in containerized environments like OpenShift, ensuring robust monitoring, scalability, and reliability.
• Innovate & Research: Actively monitor and research the latest trends, breakthroughs, and tools in AI/ML. Present findings and lead proof-of-concept projects to integrate new technologies into our stack.
• Collaborate: Work closely with senior engineers, architects, and product managers in a highly collaborative environment to translate business requirements into technical solutions.
• Atleast 6 years of relevant experience
• Python Proficiency: Strong and efficient programming skills in Python, including deep familiarity with AI-centric libraries (e.g., NumPy, Pandas, Scikit-learn).
• ML/DL Foundation: Solid understanding and practical implementation experience with machine learning algorithms and deep learning architectures (e.g., Transformers, CNNs, RNNs).
• Generative AI Experience: Demonstrable understanding of and hands-on experience with Generative AI, Large Language Models (LLMs), and Agentic AI frameworks.
• Data Expertise: Proven ability in handling and preprocessing large and complex datasets, including data cleaning, feature engineering, and validation.
• MLOps Awareness: Working experience or strong familiarity with MLOps principles, including containerizing applications using Docker and deploying on platforms like OpenShift or Kubernetes.
• Problem-Solving: Strong analytical and problem-solving skills with the ability to tackle complex challenges independently.
• The ideal candidate will have hands-on experience or deep familiarity with the following technologies:
• LLMs: Gemini, OpenAI models (GPT series), Copilot, Claude, Llama, and experience with Local Models.
• Frameworks: LangChain, LlamaIndex, and the Hugging Face ecosystem (Transformers, Datasets, Tokenizers).
• Orchestration: LangGraph and conceptual understanding of building Multi-Agent Systems.
• Development: Building production-ready services using Python, FastAPI, and asynchronous programming patterns.
• RAG (Retrieval-Augmented Generation): Advanced retrieval techniques using Vector DBs (e.g., Pinecone, Chroma) and PostgreSQL (with pgvector).
• ML/DL Platforms: PyTorch and/or TensorFlow for building and fine-tuning models.
• Deployment & Monitoring: Containerization with Docker, deploying production APIs, and implementing robust monitoring and logging.
• Applied AI Skills: Advanced Prompt Engineering, AI Workflow Design, and GenAI application optimization for cost, latency, and performance.
• Bachelor’s in Engineering degree, or equivalent work experience

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