Description

You will architect and lead enterprise-wide AI/ML and Generative AI platforms, designing scalable solutions using LLMs, Agentic AI, and RAG pipelines. You own the end-to-end ML lifecycle, from data ingestion and feature engineering to model training, deployment, and continuous monitoring.

You will build production-grade AI applications including AI Copilots, Intelligent Chatbots, and Recommendation Engines. You develop high-performance backend APIs using Python and cloud-native technologies, ensuring high availability, scalability, and security for distributed AI systems.

You lead MLOps implementation, AI governance, model versioning, and CI/CD pipelines. You integrate enterprise applications with AI services via REST APIs and event-driven architectures. You collaborate with Data Scientists and Product Managers to translate business objectives into technical solutions, while mentoring engineering teams and establishing AI engineering standards.

Responsibilities

• Architect scalable AI/ML and Generative AI platforms using LLMs, Agentic AI, and Vector Databases.
• Build production-grade AI applications such as Copilots, Chatbots, and Knowledge Assistants.
• Design and manage end-to-end ML pipelines covering data ingestion, training, deployment, and monitoring.
• Implement MLOps practices including model versioning, governance, and continuous deployment.
• Mentor engineering teams and define enterprise AI strategy and technical standards.

Required Skills

• 5+ years of experience in AI/ML engineering and architecture.
• Strong proficiency in Python and frameworks like LangChain, LlamaIndex, PyTorch, or TensorFlow.
• Experience with LLMs (OpenAI, Azure OpenAI, Anthropic, Google Gemini) and Prompt Engineering.
• Hands-on expertise in RAG pipelines, Semantic Search, and Vector Databases (Pinecone, FAISS, Milvus).
• Proficiency in cloud AI platforms: Microsoft Azure, AWS, or Google Cloud Platform (GCP).
• Experience with MLOps tools: MLflow, Kubeflow, Docker, and Kubernetes.
• Strong background in Big Data technologies: Apache Spark, PySpark, Databricks, Snowflake, and SQL.
• Ability to design distributed systems with high availability and security.

Preferred Skills

• AI/ML certifications from Microsoft Azure, AWS, Google Cloud, or Databricks.
• Experience with AI Security, Responsible AI, and Explainable AI (XAI).

Key Skills

Education

Any Gradute