AI/ML Engineer

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.\n\nCore Responsibilities\n- Develop, validate, and implement Generative AI solutions and agentic AI workflows.\n- Collaborate with data scientists and other stakeholders to understand and define project goals.\n- Maintain data infrastructure and ensure scalability and efficiency of data-related operations.\n- 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.\n- Communicate complex data findings clearly and understandably to non-technical stakeholders.\n- Adhere to data privacy and security guidelines.\n- Participate in the entire AI project lifecycle, from concept to deployment and maintenance.\n\nRequired Skills\n- Strong grasp of computer architecture, data structures, system software, and AI fundamentals.\n- 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.\n- Strong experience in implementation and fine-tuning Generative AI solutions (e.g., LLMs, multimodal models) for various business use cases.\n- Experience with Vector databases like Qdrant, ChromaDB, Pinecone, PGVector, or similar.\n- Experience in LLM integration frameworks like LlamaIndex, Ragas, etc.\n- Experience with mapping NLP models (BERT and GPT) to accelerators and awareness of trade-offs across memory, bandwidth, and compute.\n- Good to have experience with ML model lifecycle management, including training, quantization, sparsity, preprocessing, and deployment.\n- Proficiency in Python development in a Linux environment and using standard development tools.\n- Good to have experience with deep learning frameworks (such as PyTorch, TensorFlow, Keras, Spark).\n- Good to have working knowledge of machine learning and deep learning architectures, such as ANNs, CNNs, RNNs, and GANs, is a plus.\n- 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.\n- Experience deploying AI workloads on distributed systems.\n- Self-motivated team player with a strong sense of ownership and leadership.\n- Strong verbal, written, and organizational skills for effective communication and documentation.\n- Knowledge of cloud computing platforms and services, such as AWS, Azure, or Google Cloud.\n\nQualifications\n- Bachelor's or higher degree in Computer Science, Engineering, Mathematics, Statistics, Physics, or a related field.\n- 2-4 years of hands-on experience in AI/ML, with a strong preference for Generative AI and Agentic AI expertise.