Principal Engineer

About Us

AI-ready

We are looking for a Principal Engineer who can design and implement complex solutions that are scalable, future-proof and AI led.

Key Responsibilities

Solution Architecture

• Engage with clients to understand business goals, technical constraints, and AI/data maturity.
• Translate requirements into

  scalable solution blueprints

  across tech stack and infrastructure.
• Expert in microservices and design patterns.

  data, analytics, AI, and cloud

  .
• Define

  end-to-end architecture

  covering ingestion, storage, processing, analytics, ML/AI, and visualization.
• Ensure designs meet

  security, compliance, and governance

  requirements.

Data Architecture & Engineering

• Architect

  modern data platforms

  (data lakes, lakehouses, warehouses) using tools like Databricks, Snowflake, BigQuery, Redshift, etc.
• Design robust

  ETL/ELT pipelines

  , streaming architectures, and

  real-time analytics

  solutions.
• Define

  data models, metadata management, and master data strategies

  .
• Implement

  data governance frameworks

  (e.g., CDMC, DAMA DMBOK).

AI & MLOps Enablement

• Integrate

  ML/Gen AI solutions

  into the data platform architecture.
• Work with Data Scientists to

  operationalize AI models

  using MLOps best practices.
• Evaluate

  LLM integration

  , vector databases, and AI agents within enterprise architectures.

Leadership & Delivery

• Provide

  technical leadership

  to engineering teams.
• Review designs, code, and deployment plans for quality and scalability.
• Work closely with client stakeholders, delivery managers, and CTO to ensure success.

Required Skills & Experience

• 12+ years

  in data engineering, data architecture, or solution architecture roles.
• Hands-on expertise in

  cloud platforms

  (AWS, GCP, Azure) and

  IaC

  (Terraform, CloudFormation).
• Strong knowledge of

  data modeling

  ,

  SQL/NoSQL databases

  ,

  streaming tech

  (Kafka, Pub/Sub, Kinesis).
• Proven experience with

  Databricks, Snowflake, or equivalent

  modern data stack tools.
• Knowledge of

  data governance, security, and compliance

  frameworks.
• Familiarity with

  AI/ML architectures

  , MLOps tools (MLflow, Kubeflow), and Gen AI frameworks (LangChain, RAG pipelines).
• Strong communication and client-facing skills.