Lead AIML 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:

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 markets 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
the coming years we envisage more expansion to emerge as a global IT Service Provider.
Antino website - profile-

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 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
• 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:

• 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).

Required Skills:

DOCKERKUBERNETESAISYSTEM DESIGNAWSTENSORFLOWCI/CDMACHINE LEARNINGPYTORCHML