AI/ML Engineer

• Develop, validate, and implement Generative AI solutions and agentic AI workflows.
• Collaborate with data scientists and other stakeholders to understand and define project goals.
• Maintain data infrastructure and ensure scalability and efficiency of data-related operations.
• Stay abreast of the latest developments in Generative AI, Agentic AI, Large Language Models, and Emerging AI technologies, and recommend ways to implement them in everyday operations.
• Communicate complex data findings clearly and understandably to non-technical stakeholders.
• Adhere to data privacy and security guidelines.
• Participate in the entire AI project lifecycle, from concept to deployment and maintenance.
• Strong grasp of computer architecture, data structures, system software, and AI fundamentals.
• Strong experience in design, build, and optimization of agentic AI workflows that enable autonomous reasoning, decision-making, and tool interaction using frameworks like LangChain, LangGraph, CrewAI, Strands, etc.
• Strong experience in implementation and fine-tuning Generative AI solutions (e.g., LLMs, multimodal models) for various business use cases.
• Experience with Vector databases like Qdrant, ChromaDB, Pinecone, PGVector, or similar.
• Experience in LLM integration frameworks like LlamaIndex, Ragas, etc.
• Experience with mapping NLP models (BERT and GPT) to accelerators and awareness of trade-offs across memory, bandwidth, and compute.
• Good to have experience with ML model lifecycle management, including training, quantization, sparsity, preprocessing, and deployment.
• Proficiency in Python development in a Linux environment and using standard development tools.
• Good to have experience with deep learning frameworks (such as PyTorch, TensorFlow, Keras, Spark).
• Good to have working knowledge of machine learning and deep learning architectures, such as ANNs, CNNs, RNNs, and GANs, is a plus.
• Good to have experience in training, tuning, and deploying ML models for Computer Vision (e.g., ResNet), Recommendation Systems (e.g., DLRM), or related domains.
• Experience deploying AI workloads on distributed systems.
• Self-motivated team player with a strong sense of ownership and leadership.
• Strong verbal, written, and organizational skills for effective communication and documentation.
• Knowledge of cloud computing platforms and services, such as AWS, Azure, or Google Cloud.

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