Description

Key Responsibilities

• Architect and lead enterprise-wide AI/ML and Generative AI platforms.
• Design scalable AI solutions using LLMs, Agentic AI, RAG pipelines, and Vector Databases.
• Build production-grade AI applications including AI Copilots, Intelligent Chatbots, Recommendation Engines, and Knowledge Assistants.
• Design end-to-end ML pipelines covering data ingestion, feature engineering, model training, deployment, monitoring, and optimization.
• Develop high-performance AI services and backend APIs using Python and cloud-native technologies.
• Lead implementation of MLOps, AI governance, model versioning, monitoring, and continuous deployment.
• Architect distributed AI systems with high availability, scalability, and security.
• Integrate enterprise applications with AI services using REST APIs, event-driven architecture, and microservices.
• Collaborate with Data Scientists, Data Engineers, Product Managers, and Executive Leadership to deliver AI-driven business solutions.
• Mentor engineering teams, establish AI engineering standards, and drive innovation across enterprise AI initiatives.

Required Technical Skills

Artificial Intelligence & Generative AI

• Large Language Models (LLMs)
• OpenAI GPT
• Azure OpenAI
• Anthropic Claude
• Google Gemini
• Llama
• Mistral
• Prompt Engineering
• Fine-Tuning
• AI Agents
• Function Calling
• Tool Calling

Agentic AI & AI Frameworks

• LangChain
• LangGraph
• CrewAI
• AutoGen
• LlamaIndex
• Semantic Kernel
• Hugging Face

Machine Learning & Deep Learning

• Python
• Scikit-learn
• TensorFlow
• PyTorch
• NLP
• Deep Learning
• Supervised & Unsupervised Learning
• Reinforcement Learning
• Statistical Modeling
• Model Evaluation
• Feature Engineering

RAG & Vector Databases

• Retrieval-Augmented Generation (RAG)
• Pinecone
• FAISS
• ChromaDB
• Weaviate
• Milvus
• Embedding Models
• Semantic Search

Cloud AI Platforms

• Microsoft Azure
• AWS
• Google Cloud Platform (GCP)
• Azure AI Services
• Azure OpenAI
• AWS Bedrock
• Amazon SageMaker
• Google Vertex AI

MLOps & AI Engineering

• MLflow
• Kubeflow
• Docker
• Kubernetes
• CI/CD Pipelines
• Model Serving
• Model Monitoring
• AI Observability
• AI Governance

Big Data & Data Engineering

• Apache Spark
• PySpark
• Databricks
• Snowflake
• SQL
• Data Lakes
• Data Warehousing

Leadership & Architecture Expectations

• Proven experience leading enterprise AI/ML engineering and architecture teams.
• Strong expertise in designing scalable AI, ML, and GenAI platforms.
• Experience delivering production-grade AI applications using LLMs and Agentic AI frameworks.
• Ability to define enterprise AI strategy and translate business objectives into scalable AI solutions.
• Strong stakeholder management, executive communication, and technical leadership skills.
• Experience mentoring engineers, conducting architecture reviews, and driving enterprise AI adoption.

Preferred Qualifications

• AI/ML certifications from Microsoft Azure, AWS, Google Cloud, or Databricks.
• Experience with AI Security, Responsible AI, Model Governance, and Explainable AI (XAI).
• Experience building enterprise AI Copilots, Autonomous AI Agents, and Intelligent Automation solutions.
• Experience in Banking, Healthcare, Insurance, Retail, Manufacturing, or Financial Services domains.
• Strong understanding of cloud-native architectures, distributed AI systems, and enterprise digital transformation

Key Skills

Education

Any Gradute