Antino Labs - AI/ML Lead - LLM/Generative AI (Delhi Cantonment)

Position : Lead AI/ML Engineer Location : Gurgaon Experience : 5 years in AI/ML engineering, with 23 years in a lead role Job Summary We are seeking a versatile and highly skilled Lead AI/ML Engineer with deep expertise in Generative AI (GenAI) and Large Language Models (LLMs). This role requires a leader who can take full ownership of the AI lifecyclefrom initial architectural design to final production execution. You will lead the development of scalable AI-powered applications, demonstrating exceptional execution skills and the ability to deliver high-performance results under pressure in demanding production environments.

Machine

Learning & LLM Capability End-to-End ML Engineering : Build and manage comprehensive ML pipelines, including data ingestion, preprocessing, training, and evaluation using frameworks like PyTorch, TensorFlow, and Scikit-learn.

Advanced LLM Systems : Design and implement sophisticated LLM-based applications such as autonomous agents, chatbots, and complex automation tools.

Generative AI Specialization : Architect and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases like FAISS, Pinecone, or Weaviate.

Model Optimization : Fine-tune open-source and proprietary models (e.g., LLaMA, GPT) using advanced techniques like LoRA, QLoRA, or instruction tuning.

Agentic Frameworks : Develop complex agentic workflows utilizing frameworks such as LangChain or LlamaIndex.

Prompt Engineering : Implement expert-level prompt engineering, tool/function calling, and structured output generation.

Project

Ownership & Execution Full Lifecycle Ownership : Take complete accountability for the full ML and GenAI lifecycle, spanning data processing, model development, monitoring, and optimization.

Architectural Leadership : Drive strategic architectural decisions for AI platforms, ensuring they are modular, scalable, and maintainable.

Execution Excellence : Write clean, high-performance Python code following strict OOP principles and manage CI/CD pipelines for seamless project execution. Leadership & Mentoring : Act as a key technical leader, managing stakeholders and mentoring team members to ensure all project milestones are met with quality.

System Integrity : Manage model and prompt versioning, experiment tracking, and comprehensive documentation for all pipelines and workflows.

Performance Under Pressure Production Reliability : Ensure all AI systems maintain extreme scalability and performance under heavy production workloads, including both batch and real-time processing. High-Pressure Optimization : Rapidly optimize inference latency and system costs for ML and LLM systems to meet urgent business and technical requirements.

Proactive Problem Solving : Apply strong analytical thinking to address complex challenges such as system drift, hallucinations, and latency in rapid-paced environments.

Robust Guardrails : Implement and manage strict evaluation frameworks and feedback loops to maintain system quality under stress.

Qualifications Bachelors or Masters degree in Computer Science, AI, ML, or a related field. Proven expertise in Python, system design, and scalable AI/ML architecture. Deep knowledge of NLP, Computer Vision, and Deep Learning models.

Hands-on experience with Docker, Kubernetes, MLOps, and major cloud platforms (AWS, GCP, or Azure). (ref:hirist.tech) Position : Lead AI/ML Engineer Location : Gurgaon Experience : 5 years in AI/ML engineering, with 23 years in a lead role Job Summary We are seeking a versatile and highly skilled Lead AI/ML Engineer with deep expertise in Generative AI (GenAI) and Large Language Models (LLMs). This role requires a leader who can take full ownership of the AI lifecyclefrom initial architectural design to final production execution. You will lead the development of scalable AI-powered applications, demonstrating exceptional execution skills and the ability to deliver high-performance results under pressure in demanding production environments.

Machine

Learning & LLM Capability End-to-End ML Engineering : Build and manage comprehensive ML pipelines, including data ingestion, preprocessing, training, and evaluation using frameworks like PyTorch, TensorFlow, and Scikit-learn.

Advanced LLM Systems : Design and implement sophisticated LLM-based applications such as autonomous agents, chatbots, and complex automation tools.

Generative AI Specialization : Architect and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases like FAISS, Pinecone, or Weaviate.

Model Optimization : Fine-tune open-source and proprietary models (e.g., LLaMA, GPT) using advanced techniques like LoRA, QLoRA, or instruction tuning.

Agentic Frameworks : Develop complex agentic workflows utilizing frameworks such as LangChain or LlamaIndex.

Prompt