AI & Data Architect

Role Overview

We are seeking a visionary and hands-on AI & Data Architect to spearhead the design, evolution, and implementation of our next-generation data platforms and artificial intelligence ecosystems. In this role, you will bridge the gap between complex data engineering and advanced AI, mapping out blueprints for scalable data pipelines, robust

LLM operations (LLMOps), and secure enterprise knowledge graphs.

You will be the technical anchor for high-impact client engagements, driving the strategic integration of generative and agentic AI frameworks into core software development lifecycles and business processes.

Key Responsibilities

1. AI & Cognitive Architecture

• Agentic Framework Design: Design and implement AI-assisted and agent-based workflows, including multi-agent orchestration where appropriate.
• LLM Integration & LLMOps: Architect scalable infrastructures for hosting, fine- tuning, and serving Large Language Models (LLMs). Establish robust LLMOps pipelines for continuous monitoring, evaluation, and guardrails (bias, toxicity, drift).
• Cognitive Search & Retrieval: Design advanced Retrieval-Augmented Generation (RAG) systems utilizing vector databases, hybrid search, semantic routing, and metadata tagging.

2. Enterprise Data Infrastructure

• Modern Data Stack (MDS): Architect cloud-native data lakehouses, data warehouses, and mesh architectures capable of handling massive streams of structured, semi-structured, and unstructured data.
• Data Modeling & Pipelines: Define technical blueprints for real-time and batch ingestion pipelines. Build semantic layers and knowledge graphs to unify disconnected business entities.
• Performance Optimization: Ensure high availability, low latency, and cost-efficient computation for heavy data workloads and complex AI inferencing.

3. Governance, Security & Quality

• AI Safety & Compliance: Establish data privacy boundaries, anonymization protocols, and strictly secure data access matrices for AI consumption.
• Data Quality Engineering: Implement automated frameworks to continuously validate data lineage, accuracy, and operational readiness across all staging environments.

4. Technical Leadership & Consulting

• Cross-Functional Collaboration: Partner closely with DevOps, QA Directors, Engineering Managers, and Product teams to seamlessly embed AI and Data pipelines into the broader SDLC.
• Client Advisory: Translate complex technical paradigms into tangible business outcomes for executive stakeholders, assisting in pre-sales, solution scoping, and technical discovery.

Technical Skills & Qualifications

Core AI & Engineering Ecosystem

• AI Frameworks: Experience with one or more AI orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or equivalent.
• Data Technologies: High proficiency with modern data platforms (Snowflake, Databricks, BigQuery) and vector stores (Pinecone, Milvus, Weaviate, or pgvector).
• Languages & Tools: Expert-level Python, SQL, and familiarity with MLOps frameworks (MLflow, Kubeflow, Prefect, or Airflow).
• Cloud Architecture: Hands-on architecture experience in AWS, Azure, or GCP specifically around their native AI, serverless, and big data suites.

Experience & Education

• Experience: 10+ years in Data Engineering, Data Architecture, or ML Engineering, with 12 years of hands-on experience delivering production GenAI/LLM solutions.
• Industry Context: Proven track record delivering complex solutions within global enterprise verticals (e.g., BFSI, Retail/E-commerce, Auto Finance, or Manufacturing).
• Education: Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related quantitative field.

Key Performance Indicators (KPIs)

• Time-to-Production: Acceleration of AI prototype-to-production deployment cycles.
• Infrastructure Efficiency: Optimization of data pipeline compute costs and AI model inference latency.
• Architecture Reliability: Scalability, uptime, and data accuracy across production systems.