Data & AI Engineer

Ovii's Interpretation of the Role

The Data & AI Engineer will design and operate data pipelines, build and deploy machine‑learning and generative‑AI solutions, and integrate them into cloud‑native platforms using AWS services. Collaboration with CloudOps, DevOps, product and architecture teams is essential to deliver production‑grade analytics and AI capabilities for enterprise customers.

Role Snapshot

• Build and maintain scalable data pipelines
• Develop, train, and deploy ML models
• Experiment with LLMs and generative‑AI workflows
• Leverage AWS AI/ML services (SageMaker, Bedrock, etc.)
• Implement MLOps pipelines for production
• Collaborate across CloudOps, DevOps, and product teams
• Document reusable assets and best‑practice templates

Must-Have Requirements

• Python programming
• SQL & relational databases
• Data structures & algorithms
• Statistics & ML fundamentals
• Software engineering principles
• Data Engineering
• Machine Learning
• Applied AI
• Bachelor's or Master's in Computer Science, Data Science, AI, Machine Learning, Statistics, Mathematics or related technical discipline
• Bachelor's or Master's degree in a related technical discipline
• 0–2 years relevant experience

Nice-to-Have Signals

• AWS SageMaker / Bedrock / Glue / Athena / Redshift
• Spark / Kafka / Snowflake
• MLOps tools (MLflow, Kubeflow, SageMaker Pipelines)
• GenAI frameworks (LangChain, LangGraph, Hugging Face, OpenAI, Anthropic)
• Prompt engineering & RAG techniques
• Familiarity with NoSQL databases
• Exposure to large‑scale datasets
• AWS Data & AI services
• MLOps
• Generative AI frameworks
• AWS AI Practitioner
• AWS Machine Learning Engineer Associate

Work Setup

• Location: Pune, India
• Work mode: ONSITE
• Employment type: Full-Time

Not Specified in JD

• Visa sponsorship
• Salary range
• Remote eligibility
• Travel requirement
• Security clearance
• Coding test

What You'll Likely Work On

• Create and maintain ETL/ELT pipelines on AWS Glue and Athena
• Develop production‑grade ML models using SageMaker and evaluate them on real data
• Prototype and fine‑tune LLMs via Bedrock, building AI assistants and RAG applications
• Design and run MLOps workflows with SageMaker Pipelines, MLflow or Kubeflow
• Integrate AI/ML components into cloud‑native services using Lambda, S3 and OpenSearch
• Partner with CloudOps, DevOps and architecture teams to ensure reliable deployment
• Produce documentation, reusable templates and accelerators for the Data & AI practice

Good Fit If You Have

• Strong analytical mindset and problem‑solving ability
• Passion for emerging AI and cloud technologies
• Effective communication and teamwork across cross‑functional groups
• Curiosity to experiment with new tools and frameworks
• Ownership and execution‑focused attitude