AI / ML Engineer

griddynamics

Hyderabad 5 Years Exp Posted 28d ago

Key Responsibilities

Model Development & Deployment: Build, deploy, and maintain AI/ML models, with a heavy focus on Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) architectures.
AI Agent Orchestration: Design and optimize AI Agents for specialized functions like engineering onboarding and HR assistance, focusing on training systems for continuous learning.
Workflow Automation: Design and build components for step and flow automation, enabling AI assistants to initiate and execute workflows across multiple enterprise systems (e.g., creating tickets, scheduling meetings, provisioning accounts).
Data Engineering: Implement robust data ingestion, chunking, and embedding creation processes for both structured and unstructured data.
Model Optimization: Contribute to the continuous improvement of AI models through prompt engineering, versioning, tracking, and analysis of chat dialogs.
Cloud & FinOps Integration: Work within GCP to integrate AI/ML solutions and develop components for FinOps and cloud cost optimization.
Cross-Functional Collaboration: Partner with Architects, Data Scientists, and other engineers to translate design specifications into robust and scalable AI/ML solutions.
Troubleshooting: Identify and resolve issues related to AI/ML model performance, data pipelines, and system integrations.

Qualifications

Total Professional Experience: 5–7 Years
Core AI/ML Expertise: Proven experience developing and deploying AI/ML models, specifically LLM and RAG-based architectures.
Technical Proficiency: Strong programming skills in Python and hands-on experience with Vector Databases.
Cloud & Infrastructure: Experience with GCP (preferred), containerization (Docker), and orchestration (Kubernetes).
Data & Integration: Experience with data processing tools (e.g., Spark), a strong understanding of APIs, and experience with system integrations.
Software Engineering: Solid knowledge of SDLC best practices, methodologies, and version control systems (e.g., GitHub).
Preferred (Nice to Have): * Experience in FinOps and cloud cost optimization initiatives.
- Familiarity with incident response tools (e.g., PagerDuty, Opsgenie) or conversational AI frameworks.
  - Understanding of data governance, security, and compliance in AI/ML systems.