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

- Solid 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

📌 Principal AI Architect (Bengaluru)
🏢 ALTIMETRIK
📍 Bengaluru