Lead AI/ML Engineer

Position: Lead AI/ML Engineer

Job Location: Gurugram

Work Experience- 4+ years in AI/ML engineering, with 2-3 years in a lead role.

Email ID: [Confidential Information]

About Antino:

With the intention and conviction of emerging as an unparalleled IT Digital Transformation Services

platform, we at Antino Labs are known for providing impeccable software services using cutting edge

technology across the globe. Without ever compromising with the quality of our output and bringing

talent and diligence on a common platform, we have been noticed for our efficiency and reliability. With

dynamic exposure to the industry, we believe in refining and redefining our standard according to the

changes in the market's requirement. Our multiple years of experience in the industry has enabled us to

register our global presence. Presently, our branch offices are in Bangalore, UK, Dubai, Canada, and the

US. In the coming years, we envisage more expansion to emerge as a global IT Service Provider.

Antino website - https://www.antino.com/

LinkedIn profile- https://www.linkedin.com/company/antino-labs/mycompany/

Job Description:

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 lifecycle—from 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
• 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 fast-paced environments.
• Robust Guardrails: Implement and manage strict evaluation frameworks and feedback loops to maintain system quality under stress. tracking, and

Qualifications:

• Bachelor's or Master's 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).