AIML Team Lead / Technical Lead

Dotsquares
Jaipur, Rajasthan, India

We’re Hiring | AIML Team Lead / Technical Lead

📍 Jaipur, Rajasthan, India | 💼 Full-time

Are you passionate about building next-generation AI solutions and leading high-performing engineering teams? We’re looking for an AIML Team Lead / Technical Lead to drive innovation across Generative AI, LLMs, RAG, Agentic AI, and enterprise AI solutions.

Key Responsibilities

  • Design and implement scalable AI/ML architectures for enterprise applications.
  • Lead the development of ML, Deep Learning, Generative AI, LLMs, RAG pipelines, AI agents, and multi-agent workflows.
  • Build and deploy LLM-powered applications and AI automation solutions.
  • Evaluate, fine-tune, optimize, and deploy open-source and commercial LLMs.
  • Drive model optimization, monitoring, production deployment, and MLOps practices.
  • Mentor AI/ML engineers, conduct code reviews, and establish engineering best practices.
  • Collaborate with clients on solution architecture, POCs, technical proposals, and presentations.

Required Skills & Experience

  • 7+ years of experience in software development, AI/ML engineering, or related technical roles, with relevant AI/ML and technical leadership experience.
  • Strong Python programming and backend development experience using FastAPI, Flask, or Django.
  • Expertise in Machine Learning, Deep Learning, and frameworks such as PyTorch, TensorFlow, or Scikit-learn.
  • Hands-on expertise in Generative AI, LLMs, RAG, prompt engineering, embeddings, and vector databases.
  • Experience with Hugging Face Transformers, Hugging Face Hub, tokenizers, and pretrained models.
  • Knowledge of LLM fine-tuning, model evaluation, PEFT/LoRA, and open-source LLMs.
  • Experience with Agentic AI frameworks such as LangChain, LangGraph, AutoGen, or CrewAI.
  • Understanding of NLP and/or Computer Vision.
  • Experience with REST APIs, PostgreSQL/MongoDB, and cloud platforms such as AWS, Azure, or GCP.
  • Knowledge of Docker, Kubernetes, CI/CD, MLOps, and production AI deployments.

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