Principal Engineer - Data & Artificial Intelligence

Job Title : Principal Engineer - Data & Artificial Intelligence

Job Location : Bengaluru

Exp Range : 15- 30 years

Notice Period : immediate - 15 days

Role Summary

The Principal Software Development Engineer – Data & AI is a senior technical leader responsible for architecting and delivering scalable data platforms, advanced analytics systems, and AI/ML‑driven solutions. This role defines technical strategy, drives engineering excellence, and partners closely with Data Science, ML Engineering, Cloud, and Product teams to deliver intelligent, data‑driven capabilities across the organization.

Key Responsibilities

Technical Leadership & Architecture

• Define the end‑to‑end architecture for Data & AI platforms including ingestion, transformation, feature engineering, model training, model deployment, and real‑time inference.
• Lead design and implementation of distributed data systems, large-scale pipelines, and reusable AI/ML infrastructure components.
• Drive adoption of modern data and AI ecosystem standards—data governance, data quality, lineage, MLOps, security, and compliance.
• Evaluate emerging Data/AI technologies, frameworks, and cloud-native services and recommend the right-fit solutions.

Data Engineering & Platform Development

• Architect and build highly scalable batch and streaming pipelines using technologies such as Spark, Kafka, Databricks, cloud-native services, or equivalent frameworks.
• Design and optimize data models, feature stores, metadata systems, and real-time data processing layers for ML workloads.
• Build reusable libraries, tools, and components that accelerate delivery of Data & AI solutions.
• Ensure pipelines are robust, versioned, testable, observable, and cost‑efficient.

AI/ML Integration

• Work closely with Data Scientists and ML Engineers to operationalize ML models—automating training, validation, deployment, monitoring, and drift detection.
• Establish best practices for MLOps including CI/CD for ML, feature consistency, reproducibility, and scalable inference patterns.
• Design AI-enabled microservices, APIs, or platform capabilities that integrate seamlessly into products and customer-facing systems.

Strategy, Roadmap & Stakeholder Collaboration

• Partner with data science, product, architecture, and business stakeholders to define long-term Data & AI platform roadmaps.
• Translate ambiguous business and analytical requirements into scalable technical solutions.
• Lead deep architectural discussions, challenge assumptions, and ensure the team makes informed technical decisions aligned with strategic goals.

Mentorship, Culture & Cross-team Influence

• Mentor senior engineers, uplift engineering quality, and influence the broader Data & AI engineering culture.
• Facilitate learning by driving technical knowledge-sharing, best practice documentation, and architectural patterns.
• Provide technical leadership across multiple teams, guiding them through complex data and AI problem solving.

Operational Excellence

• Establish standards for reliability, performance, high availability, and SLAs/SLOs across Data & AI systems.
• Oversee observability metrics for pipelines, models, and AI-driven microservices (latency, throughput, drift, model health).
• Conduct performance tuning, cost optimization, capacity planning, and resilience improvements for large-scale data/AI workloads.

Required Qualifications

• 15+ years of software engineering experience, with 4+ years building data-intensive or AI-driven systems.
• Expert-level knowledge of distributed systems, big data frameworks, data modeling, and cloud data platforms.
• Strong proficiency in Python, Scala, Java, or equivalent languages with experience building production-grade data/AI systems.
• Deep understanding of ML lifecycle, MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Azure ML), and model deployment patterns.
• Hands-on experience with structured and semi-structured storage systems, streaming technologies, feature stores, and real-time inference.
• Strong architectural experience with cloud platforms (Azure/AWS/GCP) and infrastructure-as-code.
• Proven ability to lead technical direction for large engineering teams and complex initiatives.

Preferred Qualifications

• Experience scaling AI services, vector databases, retrieval-augmented generation (RAG), or LLM-based systems.
• Familiarity with GPU workloads, distributed training, or model optimization.
• Contributions to open-source data/ML frameworks or community initiatives.
• Strong background in security and governance of data and AI workloads.
• Experience building enterprise-grade data platforms with multi‑tenant, high‑scale requirements.

Success Indicators

• Delivery of scalable, reliable Data & AI systems powering mission‑critical applications.
• Demonstrated improvements in model lifecycle automation, pipeline reliability, and engineering velocity.
• Strong cross-team influence leading to adoption of best practices across Data, ML, and Software Engineering teams.
• Clear impact on data quality, AI model performance, and time-to-production for analytics and ML initiatives.
• Measurable uplift in engineering capability through mentorship and architectural leadership