Job Title : AI Lead

Experience : 810 Years

Location : Remote

Notice Period : Immediate Joiners Only

About the Role :

We are seeking an experienced AI Lead to design, build, and scale next-generation Agentic AI systems capable of autonomous reasoning, planning, and task execution. This role requires a strong blend of expertise in Generative AI, Machine Learning, MLOps, and cloud-native architectures.

As an AI Lead, you will drive the technical vision for intelligent AI solutions, architect multi-agent systems, lead engineering teams, and ensure successful deployment of production-grade AI applications. You will work closely with cross-functional stakeholders to deliver scalable, reliable, and innovative AI-powered products.

Key Responsibilities :

- Agentic AI & LLM Engineering :

- Design, develop, and orchestrate multi-agent AI systems capable of autonomous reasoning and decision-making.

- Architect and implement LLM-powered workflows using frameworks such as LangChain, LangGraph, CrewAI, and AutoGen.

- Develop and optimize Retrieval-Augmented Generation (RAG) pipelines for enterprise-scale AI applications.

- Design agent memory architectures, context management strategies, and long-term knowledge retention mechanisms.

- Integrate external tools, APIs, databases, and third-party services into AI agent workflows.

- Optimize prompt engineering strategies and implement advanced prompting techniques for production use cases.

- Build, train, deploy, and maintain machine learning models for classification, ranking, recommendation, anomaly detection, and predictive analytics.

- Fine-tune foundation models and LLMs using techniques such as LoRA, PEFT, quantization, and instruction tuning.

- Develop scalable NLP solutions leveraging transformers, embeddings, and vector search technologies.

- Define model evaluation frameworks using metrics such as F1 Score, Precision, Recall, AUC, and other business-specific KPIs.

- Implement feature engineering, model validation, and experimentation workflows.

- Establish end-to-end MLOps pipelines for model training, deployment, monitoring, versioning, and governance.

- Implement CI/CD workflows for AI and ML systems.

- Deploy AI solutions on cloud platforms including AWS, GCP, and Azure.

- Build scalable infrastructure using Docker, Kubernetes, Terraform, and cloud-native services.