Hiring For Senior AI/ML Cloud Engineer (Generative AI & MLOps)

Role Overview

We are seeking a highly skilled AI/ML Cloud Engineer to design and implement cutting-edge AI solutions across multiple cloud platforms. You will leverage Generative AI, Large Language Models (LLMs), and Agentic workflows to solve complex business problems. The ideal candidate will bridge the gap between experimental data science and production-grade cloud architecture.

Technical Requirements

Mandatory Skills (Must-Have)

• Core Programming: Advanced proficiency in Python for creating and integrating GenAI solutions.
• GenAI & LLM Frameworks: Hands-on experience with LangChain, LangGraph, and Hugging Face Transformers.
• Agentic AI: Proven ability to design and develop AI Agent workflows using cloud and open-source frameworks.
• Cloud Infrastructure: Experience managing AI services within a major cloud platform (preferred Google Cloud Vertex AI or AWS SageMaker/ECS).
• MLOps & Deployment: Experience with Docker containerization and cloud orchestration for deploying AI applications.
• Pipeline Development: Ability to collaborate with data engineers to build end-to-end AI pipelines and automated monitoring systems.

Qualifications- Minimum Requirements:

• Bachelors degree in a related discipline and 5+ years experience in AI/ML Cloud Engineering.
• 2+ years of experience of managing AI services within one cloud platform (preferred Google Cloud)

Preferred Skills (The "Plus" Factors)

• Advanced Fine-Tuning: Experience with LoRA and QLoRA parameter-efficient fine-tuning techniques.
• Retrieval-Augmented Generation (RAG): Expertise in building RAG frameworks using LlamaIndex and Vector Databases (e.g., FAISS, Pinecone).
• Advanced NLP: Familiarity with NLTK, SpaCy, Topic Modeling, and NER.
• Specialized Databases: Experience with NoSQL and Graph databases like MongoDB, Snowflake, or Neo4j.
• Evaluation Frameworks: Knowledge of LLM evaluation metrics such as BLEU, ROUGE, Precision@K, and MMR.
• API Development: Proficiency in building high-performance microservices using FastAPI or Flask.
• Infrastructure as Code (IaC): Ability to manage cloud resources using tools like Terraform or CloudFormation.

Key Responsibilities

• Architect GenAI Solutions: Design scalable AI systems leveraging managed cloud services.
• Production Monitoring: Implement robust systems for performance tracking, anomaly detection, and model drift analysis.
• Optimization: Use techniques like LoRA to optimize model efficiency and reduce hallucination rates.
• Collaborative Innovation: Work with cross-functional teams to maintain version control and optimize model efficiency over time.
• Knowledge Leadership: Create resources for internal knowledge transfer and define best practices for AI engineering within the organization.