Description

Architect and lead enterprise-wide AI, ML, and Generative AI platforms, owning the design of scalable solutions from data ingestion to production deployment.

This role is on-site.

Responsibilities

• Architect distributed AI systems using LLMs, Agentic AI, RAG pipelines, and Vector Databases for high availability and security.
• Build production-grade applications including AI Copilots, Chatbots, and Recommendation Engines using Python and cloud-native technologies.
• Design end-to-end ML pipelines covering feature engineering, model training, deployment, monitoring, and optimization.
• Implement MLOps, AI governance, model versioning, and continuous deployment using CI/CD pipelines.
• Mentor engineering teams and establish AI engineering standards across the organization.

Required Skills

• 5+ years of experience in Artificial Intelligence and Machine Engineering.
• Strong proficiency in Python and data engineering tools like Apache Spark, PySpark, and Databricks.
• Experience with cloud AI platforms including AWS, Azure, or GCP (e.g., SageMaker, Vertex AI, Bedrock).
• Hands-on experience with LLM frameworks such as LangChain, LlamaIndex, or Semantic Kernel.
• Knowledge of Vector Databases (Pinecone, FAISS, ChromaDB) and RAG implementation.
• Familiarity with MLOps tools like MLflow, Kubeflow, Docker, and Kubernetes.
• Experience collaborating with Data Scientists and Product Managers to deliver AI-driven solutions.
• Bachelor's degree in Computer Science or related field.

Preferred Skills

• Experience with Agentic AI frameworks (CrewAI, AutoGen) and advanced prompt engineering.
• Background in building intelligent chatbots and knowledge assistants at enterprise scale.

Key Skills

Education

Bachelor's degree