Key Responsibilities:

1. AI, ML & Generative AI Architecture:

- Define end-to-end architecture for AI/ML and Generative AI systems including data ingestion, feature engineering, model training, deployment, monitoring, and governance.

- Design and implement scalable Lakehouse-based AI platforms using Databricks and Snowflake.

- Architect solutions supporting both batch and real-time inference workloads.

- Lead the design of enterprise-grade GenAI applications using LLMs, RAG pipelines, and Agentic AI frameworks.

- Establish architectural standards, best practices, and reusable AI frameworks.

2. RAG, LLM & Agentic AI Solutions:

- Design and implement Retrieval-Augmented Generation (RAG) architectures using vector databases and knowledge pipelines.

- Architect intelligent AI agents for automation, orchestration, and decision-making workflows.

- Evaluate and integrate LLMs (OpenAI, LLaMA, etc.) for enterprise use cases.

- Optimize prompt engineering, embeddings, and context management strategies.

- Ensure scalability, accuracy, and cost optimization in GenAI deployments.

3. Data & Feature Engineering:

- Design robust data pipelines for structured and unstructured data.

- Lead feature engineering strategies for ML and AI models.

- Collaborate with Data Engineering teams to build high-performance data ingestion and transformation pipelines.

- Implement data governance, lineage, and quality frameworks.

4. Cloud & Platform Architecture:

- Architect AI solutions on cloud platforms such as AWS, Azure, or GCP.

- Design cloud-native, microservices-based AI systems.

- Leverage containerization and orchestration tools (Docker, Kubernetes) for scalable deployments.

- Implement MLOps and LLMOps best practices for CI/CD, monitoring, and lifecycle management.

5. POCs, Innovation & Technical Leadership:

- Conduct Proof of Concepts (POCs) to validate architectural approaches and design considerations.

- Analyze current product architecture and recommend AI-driven enhancements.

- Provide technical leadership and mentorship to AI, Data Science, and Engineering teams.

- Drive innovation by identifying emerging AI/GenAI trends and enterprise adoption opportunities.

- Collaborate with stakeholders, product managers, and business leaders to translate business needs into AI solutions.

6. Governance, Security & Compliance:

- Define AI governance frameworks including model monitoring, explainability, and ethical AI practices.

- Ensure compliance with data privacy and enterprise security standards.

- Implement observability, model performance tracking, and risk mitigation strategies.

Required Skills & Qualifications:

- 12+ years of experience in AI/ML architecture, Data Engineering, or Advanced Analytics.

- Strong expertise in Generative AI, LLMs, RAG, and Agentic AI architectures.

- Hands-on experience with Databricks, Snowflake, and Lakehouse architecture.

- Proficiency in Python, PySpark, and AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn).

- Experience with Vector Databases (FAISS, Pinecone, Weaviate, etc.).

- Strong knowledge of MLOps/LLMOps tools such as MLflow, Kubeflow, or Azure ML.

- Experience designing real-time and batch AI pipelines.

- Deep understanding of Feature Engineering and model lifecycle management.

- Strong experience with REST APIs, microservices, and scalable system design.