External role · Mindfire Solutions
AI/ML Engineer
India · Remote · Full-time
About this role
Architect, develop, and deploy production-grade Generative AI and Agentic AI solutions — LLM-powered systems, autonomous workflows, and intelligent automation.
About the role Architect, develop, and deploy production-grade Generative AI and Agentic AI solutions, including LLM-powered systems, autonomous workflows, and intelligent automation platforms. Core 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 to non-technical stakeholders. • Adhere to data privacy and security guidelines. • Participate in the entire AI project lifecycle, from concept to deployment and maintenance. What they are looking for • Strong grasp of computer architecture, data structures, system software, and AI fundamentals. • Experience designing, building, and optimizing agentic AI workflows (autonomous reasoning, decision-making, and tool interaction) using frameworks such as LangChain, LangGraph, CrewAI, or Strands. • Experience implementing and fine-tuning Generative AI solutions (LLMs, multimodal models) for business use cases. • Experience with vector databases such as Qdrant, ChromaDB, Pinecone, or PGVector. • Experience with LLM integration frameworks such as LlamaIndex and Ragas. • Experience mapping NLP models (BERT and GPT) to accelerators, with awareness of memory, bandwidth, and compute trade-offs. • Proficiency in Python development in a Linux environment. • Experience deploying AI workloads on distributed systems. • Knowledge of cloud platforms such as AWS, Azure, or Google Cloud. Nice to have • ML model lifecycle management: training, quantization, sparsity, preprocessing, and deployment. • Deep learning frameworks: PyTorch, TensorFlow, Keras, or Spark. • Familiarity with ANNs, CNNs, RNNs, and GANs. • Training, tuning, and deploying models for computer vision (e.g. ResNet) or recommendation systems (e.g. DLRM). 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. Work mode Full-time, remote, India.