Description

Key Responsibilities

• Lead the design and implementation of enterprise-scale Generative AI, Machine Learning, and Agentic AI solutions.
• Architect and develop AI-powered applications using LLMs, RAG frameworks, AI Agents, and Vector Databases.
• Design end-to-end ML pipelines covering data ingestion, feature engineering, model training, deployment, monitoring, and optimization.
• Build intelligent AI assistants, copilots, chatbots, recommendation engines, and knowledge management solutions.
• Develop scalable backend AI services and APIs using Python and cloud-native technologies.
• Implement MLOps frameworks for model versioning, deployment automation, governance, and observability.
• Integrate enterprise systems with AI platforms using APIs, event-driven architectures, and microservices.
• Collaborate with Data Scientists, Data Engineers, Architects, Product Teams, and Business Stakeholders.
• Drive AI innovation, technology evaluation, proof-of-concepts, and enterprise AI adoption strategies.
• Mentor AI/ML engineers and establish engineering standards and best practices.

Required Technical Skills

Generative AI & LLMs

• OpenAI GPT
• Azure OpenAI
• Anthropic Claude
• Google Gemini
• Llama
• Mistral
• Prompt Engineering
• Fine-Tuning
• Function Calling
• Tool Calling
• AI Agents

Agentic AI Frameworks

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

RAG & Knowledge Systems

• Retrieval-Augmented Generation (RAG)
• Embedding Models
• Vector Search
• Document Intelligence
• Enterprise Knowledge Management

Machine Learning & Deep Learning

• Scikit-Learn
• TensorFlow
• PyTorch
• NLP
• Deep Learning
• Supervised & Unsupervised Learning
• Feature Engineering
• Model Evaluation
• Statistical Analysis

Programming

• Python
• Pandas
• NumPy
• FastAPI
• Flask
• REST APIs
• Microservices

Vector Databases

• Pinecone
• FAISS
• ChromaDB
• Weaviate
• Milvus

Cloud Platforms

• AWS
• Azure
• GCP
• AWS Bedrock
• Azure AI Services
• Vertex AI
• SageMaker

MLOps & AI Operations

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

Big Data & Analytics

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

Leadership & Architecture Expectations

• Proven experience leading enterprise AI/ML engineering teams.
• Strong expertise in architecting scalable AI and GenAI platforms.
• Experience delivering production-grade LLM and Agentic AI solutions.
• Ability to translate business requirements into enterprise AI solutions.
• Strong stakeholder management and executive communication skills.
• Experience mentoring engineers and driving AI engineering best practices.

Preferred Qualifications

• Experience building enterprise AI Copilots and Autonomous Agent Systems.
• Exposure to Multi-Agent Architectures and AI Orchestration Platforms.
• Experience with Responsible AI, AI Security, and Governance frameworks.
• Azure AI Engineer, AWS Machine Learning, Databricks AI, or Google AI Certifications.
• Experience in BFSI, Healthcare, Insurance, Retail, Telecom, or Financial Services domains

Key Skills

Education

Bachelor's degree