Head of Artificial Intelligence

Job Description – Head of AI, ML & Generative AI

Position Head – AI, Machine Learning & Generative AI

Location- Trivandrum, Kerala

Job Summary

We are seeking an experienced AI leader with 15–20 years of overall IT experience, including 10+ years in Artificial Intelligence, Machine Learning, Data Science, and Generative AI, to lead our Enterprise AI practice. The candidate will define the AI strategy, establish AI Centers of Excellence (CoE), drive innovation, and deliver scalable AI platforms and products across the organization. This role requires strong leadership, enterprise architecture expertise, business acumen, and experience managing large AI engineering teams while collaborating with executive leadership and business stakeholders.

Key Responsibilities

• Define and execute the enterprise AI, ML, and Generative AI strategy and roadmap.
• Lead AI architecture, platform engineering, and cloud-native AI solution design.
• Drive implementation of LLMs, RAG, Agentic AI, Multi-Agent Systems, NLP, Computer Vision, and Intelligent Automation solutions.
• Establish enterprise AI governance, Responsible AI, MLOps, model lifecycle management, security, and compliance frameworks.
• Lead AI platform development using AWS, Azure, GCP, OCI, Kubernetes, Docker, MLflow, SageMaker, Vertex AI, Databricks, LangChain, LangGraph, and vector databases.
• Build and mentor high-performing AI, Data Science, Data Engineering, and MLOps teams.
• Partner with business leaders to identify AI opportunities and deliver measurable business value.
• Drive enterprise architecture reviews, innovation initiatives, technology partnerships, and AI best practices.
• Own end-to-end delivery of enterprise AI programs, including planning, budgeting, governance, and stakeholder management.

Preferred Skills

• Artificial Intelligence, Machine Learning, Deep Learning, Generative AI
• LLMs, RAG, Agentic AI, Prompt Engineering, Multi-Agent Systems
• Python, TensorFlow, PyTorch, Scikit-Learn, Hugging Face
• LangChain, LangGraph, LlamaIndex
• Pinecone, ChromaDB, Weaviate, Milvus, FAISS
• AWS, Azure, GCP, OCI, Kubernetes, Docker, Terraform
• MLOps, MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, Databricks
• AI Governance, Responsible AI, Explainable AI (XAI), AI Security

Qualification

• B.E./ B. Tech. /M.Tech./MCA in Computer Science, Artificial Intelligence, Data Science, Information Technology, or a related discipline.
• Relevant AI, Cloud, TOGAF, or Architecture certifications are preferred.

Experience

• 15–20 years of overall IT experience.
• Minimum 10+ years of hands-on experience in AI/ML and Data Science.
• 8+ years of experience leading enterprise AI teams and large-scale AI transformation programs.
• Experience in Banking, NBFC, FinTech, Insurance, or Financial Services is highly preferred.

Enterprise AI Solution Architecture

Provide strategic architecture leadership for enterprise AI platforms covering:

• Data Engineering
• Feature Engineering
• Machine Learning Platforms
• Deep Learning
• Model Serving
• Model Monitoring
• AI APIs
• Enterprise Integration
• Event-Driven AI Architecture
• AI Microservices
• AI Platform Engineering
• Intelligent Automation Platforms

Design scalable, secure, cloud-native AI architectures capable of supporting enterprise-scale workloads.

Generative AI & Agentic AI Leadership

Lead enterprise adoption and implementation of:

• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Agentic AI Platforms
• Multi-Agent Systems
• AI Agents
• Prompt Engineering
• AI Workflow Automation
• Autonomous Decision Systems
• AI Copilots
• Enterprise Knowledge Assistants
• Conversational AI Platforms
• Intelligent Search Platforms

Experience with leading AI ecosystems including:

• LangChain
• LangGraph
• LlamaIndex
• Haystack
• OpenAI
• Anthropic Claude
• Google Gemini
• Meta Llama
• Mistral AI
• AWS Bedrock
• Azure OpenAI

Machine Learning, Deep Learning & Data Science

Provide technical leadership for enterprise AI solutions including:

• Predictive Analytics
• Classification
• Regression
• Recommendation Engines
• Forecasting
• Risk Models
• Fraud Detection
• Customer Segmentation
• Churn Prediction
• Credit Scoring
• NLP Solutions
• Computer Vision
• OCR & Document Intelligence
• Speech AI
• Time-Series Forecasting

Strong expertise in:

• Python
• Scikit-Learn
• TensorFlow
• PyTorch
• XGBoost
• LightGBM
• Hugging Face
• Pandas
• NumPy

Enterprise RAG & Knowledge Systems

Architect enterprise-scale knowledge platforms using:

• Vector Databases
• Knowledge Graphs
• Semantic Search
• Hybrid Search
• Embedding Pipelines
• Enterprise Document Intelligence
• AI Search Platforms

Hands-on knowledge of:

• Pinecone
• ChromaDB
• Weaviate
• Milvus
• FAISS
• Elasticsearch
• OpenSearch

AI Platform Engineering & MLOps

Define enterprise AI platform standards for:

• Model Lifecycle Management
• AI Governance
• Model Registry
• Feature Stores
• Drift Detection
• Model Monitoring
• AI Observability
• Continuous Training
• Continuous Deployment

Platforms:

• MLflow
• Kubeflow
• Airflow
• SageMaker
• Vertex AI
• Databricks
• Azure ML

Implement enterprise CI/CD and DevSecOps practices for AI solutions.

AI Governance, Risk & Responsible AI

Establish enterprise AI governance covering:

• Responsible AI
• Explainable AI (XAI)
• AI Ethics
• AI Security
• Model Auditing
• AI Risk Management
• Bias Detection
• Compliance Frameworks
• Regulatory AI Controls
• Data Privacy
• Enterprise Security Standards

Ensure compliance with financial sector and global AI regulations.

Leadership, People & Delivery

• Build and lead high-performing AI, ML, Data Engineering, and MLOps teams.
• Mentor Architects, AI Engineers, Data Scientists, and Engineering Managers.
• Establish engineering excellence and AI best practices.
• Drive architecture governance, design reviews, and technical quality.
• Own enterprise AI delivery across multiple programs.
• Collaborate with CXOs, Product Owners, Business Heads, and external partners.
• Manage AI budgets, vendor relationships, and strategic technology partnerships.
• Drive innovation, patents, accelerators, and reusable AI assets.

Preferred Certifications

• AWS Certified Machine Learning – Specialty
• AWS Certified Solutions Architect – Professional
• Microsoft Azure AI Engineer Associate
• Google Professional Machine Learning Engineer
• Databricks Certified Professional
• Kubernetes (CKA/CKAD)
• TOGAF Certification
• PMP / PMI-ACP / Scrum Certification