AI Engineer

Bangalore Hybrid

Role Overview:
As the  AI Engineer, you will be responsible for designing, training, and deploying AI models, with a specific focus on LLMs and agent-based systems. Youll work across the entire AI pipeline, from identifying use cases to deploying and monitoring models in production. This role is ideal for a technically skilled individual contributor who can independently drive innovation and deliver impactful solutions.

Key Responsibilities:
LLM & NLP Model Development: Develop and fine-tune large language models (GPT-3/4, T5, BERT, etc.) for tasks like text generation, summarization, sentiment analysis, and information retrieval.
Agentic System Design: Build and deploy autonomous, goal-oriented agentic systems capable of interacting with complex environments to achieve specific tasks.
Project Ownership: Manage AI initiatives from ideation through to production deployment, independently handling all aspects of model lifecycle management.
Cross-Functional Collaboration: Partner with teams across product, engineering, and operations to translate business requirements into actionable AI projects, particularly in areas that leverage LLMs and agentic AI.
Model Optimization & Evaluation: Optimize models for scalability, accuracy, and efficiency; perform regular testing, monitoring, and tuning to maintain performance in production.
Research & Experimentation: Stay abreast of the latest AI advancements, including prompt engineering, reinforcement learning, and advancements in agent-based architectures, and evaluate their applicability.
Documentation & Knowledge Sharing: Document architectures, model performance, and lessons learned, while actively sharing insights and best practices across the organization.

Requirements:
Experience: 4+ years in AI and machine learning, with hands-on expertise in LLMs and agentic system development and deployment.
Proficiency in NLP & LLMs: Strong experience with large language models (GPT-3/4, Claude etc.) and understanding of transformer architectures, pretraining/fine-tuning, prompt engineering, and task adaptation.
Agentic Systems Expertise: Knowledge in creating and deploying agentic systems with a goal-oriented design, leveraging reinforcement learning (RL) or other methods for autonomous task management.
Technical Skills: Advanced proficiency in Python, ML libraries (Hugging Face Transformers, TensorFlow, PyTorch), and data processing libraries (Pandas, NumPy).
MLOps & Deployment: Experience with MLOps practices, particularly for deploying large models and agentic systems in production environments (Docker, Kubernetes, MLFlow, or similar).
Data Management: Familiarity with data engineering principles, including handling large datasets and real-time processing for model training, and experience with cloud platforms (AWS, GCP, Azure).
Independent Problem Solving: Strong analytical skills with a proven track record of working independently to solve complex AI challenges.
Communication Skills: Ability to clearly articulate technical concepts and AI-driven insights to non-technical stakeholders and document work for continuity.

Budget - 16 LPA