AI/ML Engineer - Generative AI & Agentic Systems

• Design, develop, and deploy scalable AI and machine learning solutions for real-world applications.
• Build and optimize Generative AI applications leveraging Large Language Models (LLMs).
• Develop Agentic AI and multi-agent workflows using modern orchestration frameworks.
• Design and implement Retrieval-Augmented Generation (RAG) architectures with semantic search capabilities.
• Engineer prompts and optimize LLM outputs for accuracy, reliability, and business outcomes.
• Fine-tune, evaluate, and benchmark AI models for domain-specific use cases.
• Develop custom machine learning models for classification, clustering, recommendation systems, and predictive analytics.
• Perform feature engineering, model selection, experimentation, and optimization.
• Build end-to-end ML pipelines and manage model lifecycle processes using MLOps practices.
• Collaborate with cross-functional teams to integrate AI solutions into production environments.
• Monitor model performance, conduct evaluations, and continuously improve deployed systems.

• AI / Machine Learning
• Generative AI and Large Language Models (LLMs)
• Agentic AI and Multi-Agent Systems
• Retrieval-Augmented Generation (RAG)
• Semantic Search Architectures
• Prompt Engineering
• Fine-Tuning and Model Evaluation
• Custom Machine Learning Model Development
• Classification Models
• Clustering Techniques
• Recommendation Systems
• Predictive Analytics
• Feature Engineering
• MLOps and Model Lifecycle Management
• Frameworks & Libraries
• LangGraph
• LangChain
• CrewAI (preferred)
• AutoGen
• MCP (Model Context Protocol)
• OpenAI APIs
• Anthropic Claude APIs
• Hugging Face Ecosystem
• Scikit-Learn
• XGBoost
• PyTorch

Preferred Qualifications

• Experience building production-grade AI systems using cloud-native architectures.
• Understanding of multi-agent orchestration and workflow automation.
• Experience with model evaluation, observability, and AI system monitoring.
• Strong software engineering fundamentals and API integration experience.
• Familiarity with scalable deployment patterns for AI applications.

Nice to Have

• Experience with vector databases and embedding pipelines.
• Exposure to distributed training or large-scale inference systems.
• Experience with experimentation frameworks and A/B testing for AI systems.
• Knowledge of responsible AI practices and model governance.
• Ideal Candidate Profile
• Strong analytical and problem-solving skills.
• Ability to translate business requirements into AI solutions.
• Comfortable working across research, experimentation, and production environments.
• Passionate about emerging AI technologies and continuous learning.