Job Description
<p><p><b>Description : </b><br/><br/>Job brief : <br/><br/>An ideal candidate will have 7 to 12 years of experience working in live projects related to Artificial : </b><br/><br/>- Lead the design and implementation of enterprise-grade Machine Learning, Deep Learning and Generative AI solutions.<br/><br/>- Define and execute the AI technology projects, aligning Generative AI, Machine Learning, and Deep Learning initiatives with business strategy and data architecture.<br/><br/>- Design, evaluate, and guide development of autonomous and agentic AI systems capable of reasoning, planning, and tool-based decision-making.<br/><br/>- Architect end-to-end RAG pipelines including document processing, embedding generation, vector storage, retrieval mechanisms, and contextual response systems.<br/><br/>- Oversee Machine Learning and Deep Learning model development, training, deployment, and optimization for predictive and generative use cases.<br/><br/>- Establish robust LLMOps / MLOps pipelines ensuring model lifecycle management, observability, version control, and performance monitoring.<br/><br/>- Collaborate with data engineering, analytics, and DevOps teams to integrate AI systems with enterprise data warehouse/lakes, APIs, and reporting environments.<br/><br/>- work with stakeholders to identify, evaluate, and prioritize AI-driven business opportunities, focusing on measurable impact and innovation.<br/><br/>- Provide technical leadership, mentorship, and guidance to AI engineers, data scientists, and developers, fostering a high-performance innovation culture.<br/><br/>- Stay ahead of emerging AI research, frameworks, and tools, driving continuous evolution of enterprise AI capabilities.<br/><br/><b>Skills Areas : </b><br/><br/>- Deep expertise in Generative AI, Large Language Models (GPT, Claude, Llama, Mistral, etc.), and RAG architectures.<br/><br/>- Strong proficiency in Agentic AI concepts - autonomous agents, multi-agent orchestration, and reasoning loops.<br/><br/>- Advanced understanding of Machine Learning (ML) and Deep Learning (DL) algorithms, architectures (Transformers, CNNs, RNNs), and model deployment workflows.<br/><br/>- Experience architecting solutions with AWS agentic AI framework, LangChain, LlamaIndex, Semantic Kernel, or similar frameworks.<br/><br/>- Hands-on experience with RDBMS, No SQL and vector databases.<br/><br/>- Proficiency in Python, scikit-learn, PyTorch, TensorFlow, and Hugging Face Transformers.<br/><br/>- Expertise in LLMOps / MLOps practices model training, CI/CD, observability, and drift management.<br/><br/>- Deep understanding of cloud-native AI architecture (AWS Sagemaker, Azure AI, GCP Vertex AI).<br/><br/>- Strong foundation in AI governance, ethical AI, and compliance frameworks.<br/><br/>- Excellent leadership, communication, and stakeholder management skills to align AI initiatives with business goals.<br/><br/><b>Good-to-Have Skills : </b><br/><br/>- Experience in Data Engineering ETL/ELT pipeline design, data modeling, data warehousing, and integration with AI pipelines.<br/><br/>- Familiarity with Data Visualization and BI tools such as Power BI, Tableau, Amazon QuickSight especially for building analytical insights from AI-driven data.<br/><br/>- Understanding of DataOps and analytics architectures supporting AI and decision intelligence systems.<br/><br/>- Knowledge of enterprise data platforms and API-based data integration to connect AI models with structured and unstructured data sources.<br/><br/><b>Experience : </b><br/><br/>- The ideal candidate will have relevant experience of > 7 years.<br/><br/>- Possession of a professional degree / post-graduation is desirable.<br/><br/>- Certifications in the respective technology areas will be an added advantage.<br/><br/></p><br/></p> (ref:hirist.tech)