Job Summary:
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.

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.

Robust Guardrails: Implement and manage strict eva

📌 Ai/ml Lead Gurugram
🏢 Antino Labs Private
📍 Gurugram