Job Description & How to Apply Below
- Gen AI Engineer
Key Skills required - Python, GCP, Gen AI, Vertex AI, LLM, etc.
Experience- 4-12yrs
Job Location - Chennai/Hyderabad
Mode of Work - Hybrid
About the Role:
As a Premier Google Cloud Partner, we sit at the cutting edge of AI innovation, delivering production-grade Generative AI and machine learning solutions for enterprise clients.
We are seeking a results-driven AI Engineer to build our next generation of AI-powered products. In this role, you will take full ownership of developing advanced AI systems, focusing on LLM Fine-Tuning using techniques like LoRA/QLoRA and inference optimization through quantization.
A key part of your role will be designing and implementing autonomous AI Agents and complex reasoning workflows with frameworks like Lang Chain or Llama Index.
You will build robust Retrieval-Augmented Generation (RAG) pipelines from the ground up, leveraging vector databases, and will be responsible for rigorous model evaluation to ensure state-of-the-art performance and accuracy.
Required Skills / Experience
Elite proficiency in Python; proficient in SQL, Docker, and Git.
Practical experience with Cloud Run, GKE, Big Query, Cloud Storage, and Pub/Sub.
Hands-on experience with Vertex AI & Gemini Enterprise Pipelines, GenAI Studio, Endpoints, and Advanced Vector Search.
Proven experience building RAG systems or LLM applications using Lang Chain, Llama Index, or native GCP GenAI tools such as the Gemini API.
Experience with LLM fine-tuning, parameter-efficient techniques (PEFT) like LoRA/QLoRA, and model optimization for inference via quantization.
Experience building pipelines with Apache Beam/Dataflow, Spark, or similar.
Familiarity with Terraform for provisioning AI/ML infrastructure.
Knowledge of or experience with Knowledge Graphs (e.g., Neo4j, Spanner) for structured data retrieval in AI systems.
Roles & Responsibilities :
Own the design, development, deployment, and maintenance of production-grade AI microservices. Take pride in seeing your code go from local testing to a highly scalable production environment.
Develop, optimize, and deploy robust Generative AI applications, specifically focusing on Retrieval-Augmented Generation (RAG) systems, agentic workflows, and prompt engineering using the Gemini API and Vertex AI Search.
Independently lead the fine-tuning of foundation models using Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA/QLoRA. You will be responsible for the entire customization workflow, from data preparation to deploying the adapted model for specific business tasks, including optimizing models for inference via quantization.
Build and optimize scalable data ingestion and processing pipelines using Big Query and Dataflow (Apache Beam) to support real-time and batch AI inference.
Productionize ML/GenAI models by building CI/CD and MLOps pipelines using Vertex AI & Gemini Enterprise Pipelines, Cloud Build, and Terraform. Ensure models are securely hosted on Vertex AI Endpoints or Cloud Run.
Design and implement robust evaluation frameworks to continuously measure and improve model performance, accuracy, and relevance. You will establish automated monitoring for key safety metrics, including drift, bias, and hallucination detection, ensuring our AI solutions are not just powerful but also trustworthy and reliable.
Write highly efficient Python code and configure GCP infrastructure (e.g., Cloud Run, Big Query, GCS) to ensure optimal cost management, low latency, and high availability.
Act as the security champion for our AI systems. Implement security best practices for protecting sensitive data throughout the model lifecycle, manage data governance, and ensure that all AI solutions adhere to privacy standards and compliance requirements.
Stay at the forefront of the rapidly evolving AI landscape. Proactively research and evaluate state-of-the-art techniques, advanced models, and research papers, translating cutting-edge concepts into actionable prototypes and strategic recommendations for new product features.
Work closely with product managers, and business stakeholders to translate business requirements into technical specifications. You will be responsible for communicating complex AI concepts and project outcomes clearly to both technical and non-technical audiences.
Thrive in an agile, project-based environment. You should be comfortable pivoting between classic machine learning tasks and modern LLM orchestration as client needs dictate.
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