Data & AI Lead

Position:

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Role Overview:

We are seeking a highly experienced and technically strong Data & AI Lead to take ownership of our data and AI initiatives across the enterprise. This position will be responsible for designing and executing a long-term roadmap to transform the organization into a data-first, AI-powered enterprise. The role encompasses data architecture, advanced analytics, AI/ML engineering, MLOps, governance, and applied AI use case delivery.

The successful candidate will bring a unique combination of deep technical expertise, proven leadership experience, and strategic vision—bridging the gap between technical innovation and business value creation.

Key Responsibilities:

1. Data & AI Strategy

● Define and implement the enterprise-wide data and AI strategy, aligning with strategic business goals and digital transformation programs.

● Establish a multi-year roadmap encompassing data platforms, AI/ML adoption, AI governance frameworks, and responsible AI practices.

● Evaluate and recommend cutting-edge AI technologies (Generative AI, LLMs, RAG-based systems, multi-modal AI, AutoML, federated learning, reinforcement learning).

● Partner with senior leadership to identify business-critical AI opportunities and prioritize proof-of-concepts leading to large-scale deployments.

2. Data Architecture & Engineering

● Lead the architecture and modernization of enterprise data platforms (data lakes, data warehouses, data mesh, and real-time streaming architectures).

● Oversee development of ETL/ELT pipelines, ensuring usability, scalability, fault-tolerance, and low-latency performance for AI/analytics workloads.

● Drive initiatives in master data management (MDM), data quality frameworks, data cataloging, and metadata-driven data discovery.

● Collaborate with cloud engineering teams to design and optimize cloud-native data infrastructure on AWS (Redshift, SageMaker, Glue), Azure (Synapse, ML Studio, Fabric), or GCP (BigQuery, Vertex AI).

3. AI/ML Development & Deployment

● Oversee design, training, optimization, and deployment of machine learning and deep learning models across diverse business domains (forecasting, personalization, predictive maintenance, fraud detection, NLP, computer vision, and GenAI-enabled use cases).

● Define end-to-end AI model lifecycle processes: from feature engineering, model experimentation, hyperparameter tuning, to production-grade MLOps pipelines.

● Implement MLOps frameworks for continuous training, CI/CD for models, and automated monitoring of model drift, bias, accuracy decay, and compliance.

● Introduce model observability tools (Weights & Biases, MLflow, Kubeflow, Seldon, AWS Sagemaker Model Monitor, Azure ML pipelines).

4. AI & Data Governance

● Establish AI governance frameworks, ensuring compliance with global AI and data regulations (GDPR, HIPAA, CCPA, EU AI Act, Responsible AI mandates).

● Define principles for AI ethics, fairness, transparency, and explainability—embedding them into model development and deployment processes.

● Implement data governance procedures, including data lineage, data stewardship programs, auditing, and access management policies.

● Set clear guidelines for data ownership, classification, retention, and usage policies.

5. Applied AI & Business Solutions

● Collaborate with internal business units and stakeholders to identify opportunities for AI adoption in customer experience, supply chain, finance, risk, sales, marketing, and operations.

● Deliver enterprise-scale AI-driven applications (chatbots, recommender systems, document intelligence systems, fraud detection engines, digital twins, robotics process automation with AI).

● Develop Generative AI applications, leveraging foundation models (OpenAI, Anthropic, Llama, Falcon, Mistral, Hugging Face models) and customize them for domain-specific applications (summarization, knowledge extraction, contract analysis, conversational AI).

● Apply advanced optimization strategies such as RLHF (Reinforcement Learning with Human Feedback) and fine-tuning/federated learning for secure enterprise AI adoption.

6. Collaboration & Leadership

● Build, lead, and mentor a team of data engineers, ML engineers, AI researchers, data scientists, MLOps specialists, and analytics professionals.

● Foster a culture of continuous learning, experimentation, and innovation in AI and data science.

● Conduct regular knowledge-sharing sessions on AI research papers, emerging methods, and applied trends in the industry.

● Partner with CIOs, Chief Product Officers, Heads of Business Units to ensure AI and data science solutions align with evolving strategic needs.

7. Emerging Technology & Innovation

● Evaluate multi-modal AI systems (text, image, audio, video, sensor data).

● Explore edge AI and on-device intelligence to support IoT, AR/VR, autonomous systems.

● Pilot AI Agents and autonomous decision-making systems, integrated with knowledge graphs and reasoning engines.

● Identify partnerships, academic research collaboration, and vendor solutions that enhance the AI capabilities of the enterprise.

Technical Competencies Required:

● Programming & Frameworks: Python, R, Java/Scala; frameworks like TensorFlow, PyTorch, Scikit-learn, Hugging Face.

● Big Data & Databases: Spark, Hadoop, Snowflake, BigQuery, Redshift, Synapse, Databricks, Delta Lake.

● Cloud & MLOps Stack: AWS Sagemaker, Azure ML, GCP Vertex AI; MLflow, Kubeflow, Airflow, Seldon Core, Argo Workflows, Docker, Kubernetes.

● Data Streaming & Messaging: Kafka, Kinesis, Pulsar.

● NLP & LLMs: Tokenization, embeddings, transformers, fine-tuning, retrieval-augmented generation (RAG), vector databases (Pinecone, Weaviate, Milvus, FAISS).

● Visualization & BI: PowerBI, Tableau, Superset, Looker, custom dashboards.

● AI Governance Tools: Model interpretability (SHAP, LIME, ELI5), bias detection frameworks, drift monitoring tools.

Qualifications :

● Bachelor’s/Master’s/PhD in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or related field.

● 6–8+ years of progressive experience in data engineering, AI/ML engineering, and advanced analytics, with at least 5+ years in senior leadership roles.

● Proven track record of delivering enterprise-scale AI projects into production and operationalizing AI systems.

● Demonstrated ability to lead cross-functional teams and align data/AI initiatives with business outcomes. ● Strong publication, open-source contribution, or conference participation in AI/ML fields is a plus

Key Personal Attributes:

● Visionary leader with ability to balance technical depth and executive-level communication.

● Entrepreneurial mindset with ability to take calculated risks for innovation.

● Strong ethical orientation and commitment to responsible AI practices.

● Collaborative, with an ability to influence senior stakeholders and inspire teams.

What We Offer:

● Opportunity to shape the future of AI adoption in a growing and innovation-driven enterprise.

● Exposure to state-of-the-art technologies and enterprise-grade AI/ML platforms.

● Chance to build a world-class Data & AI team with autonomy to innovate.

● Competitive compensation, performance-based incentives, and benefits package..