AI/ML Engineer

<div> As an AI/ML Engineer, you will be responsible for architecting, developing, and deploying production-grade Generative AI and Agentic AI solutions, including LLM-powered systems, autonomous workflows, and intelligent automation platforms that drive scalable business innovation. Collaborate closely with cross-functional teams, including data scientists, to recognize and establish project objectives. Oversee data infrastructure maintenance, ensuring streamlined and scalable data operations. Stay updated with advancements in Generative AI, Agentic AI, and broader AI technologies, proposing their integration for operational enhancement. Effectively convey detailed data insights to non-technical stakeholders. Uphold stringent data privacy and security protocols. Engage in the full lifecycle of AI projects, spanning from ideation through deployment and continuous upkeep.</div> Responsibilities - 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. Qualifications - Bachelor's or higher degree in Computer Science, Engineering, Mathematics, Statistics, Physics, or a related field. - 2-4 years of hands-on experience in AI/ML, with a strong preference for Generative AI and Agentic AI expertise. Required Skills - - 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.