Infra AI Automation Lead

Infra AI Automation Lead

• At least 5+ years of programming experience in Python • Hands-on experience delivering end-to-end Gen AI solutions • Strong experience with LLMs (OpenAI, Azure OpenAI, Hugging Face, Anthropic, etc.) • Hands-on experience with TensorFlow, PyTorch, LangChain, LlamaIndex and Prompt Engineering • Experience building Agentic AI systems and multi-agent frameworks (LangGraph, CrewAI, AutoGen, Semantic Kernel, etc.) • Experience with vector databases (FAISS, Pinecone, Weaviate, Chroma) and RAG pipelines • Working knowledge of MLOps / LLMOps practices — CI/CD, model versioning, monitoring and deployment • Familiarity with cloud platforms (Azure / AWS / GCP) and containerization (Docker, Kubernetes) • Experience mentoring or technically guiding junior engineers • Good knowledge of deep learning, advanced NLP, data structures, SQL & NoSQL • Understanding of responsible AI and ethical AI frameworks • Strong communication, analytical and problem-solving skills Besides the professional qualifications of the candidates, we place great importance in addition to various forms personality profile. These include: • High analytical skills • A high degree of initiative and flexibility • High customer orientation • High quality awareness • Excellent verbal and written communication skills As an Infra AI Automation Lead, you will take ownership of building and delivering AI automation solutions while guiding a team of engineers. You will be expected to stay hands-on — designing, developing, and deploying AI systems using Python, deep learning, and generative models — while also taking responsibility for the team's technical direction, code quality, and delivery outcomes. Beyond execution, you will work closely with architects and business stakeholders to ensure what is built is aligned to real needs and built to last. Client Engagement and Needs Analysis: • Lead client meetings and workshops to understand business objectives and identify Gen AI use cases • Assess client technology infrastructure, data landscape, and AI maturity to recommend adoption approaches • Translate business requirements into clear technical problem statements for internal teams. Gen AI Strategy and Solution Design: • Design and deliver end-to-end Gen AI solutions — LLM applications, RAG pipelines, fine-tuned models, and agentic workflows • Define Agentic AI architectures using frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, or Semantic Kernel • Recommend appropriate platforms, tools, and APIs based on client needs and develop implementation roadmaps with clear milestones • Ensure solutions are scalable and integrate effectively with existing enterprise systems (ERP, CRM, Data Lakes). MLOps / LLMOps and Model Lifecycle: • Establish MLOps / LLMOps practices — CI/CD, model versioning, observability, and cost optimization • Oversee end-to-end model lifecycle from training through deployment and monitoring • Implement guardrails, feedback loops, and perform statistical analysis to drive continuous improvement Technical Guidance and Implementation Support: • Provide technical guidance to AI/ML engineers and review code, model configurations, and solution designs • Mentor junior engineers through design reviews, pairing, and structured feedback • Collaborate with architects to break down high-level designs into actionable engineering tasks • Drive data preparation, fine-tuning workflows, validation strategies, and model evaluation pipelines