Lead Data & AI Engineer

About Comprinno Comprinno is a NASSCOM-incubated company headquartered in Bangalore, with offices in Pune, Coimbatore, and the United States. We specialize in cloud transformation, DevOps, infrastructure automation, and Artificial Intelligence, enabling organizations to build scalable, secure, and high-performing cloud environments on AWS. Our flagship SaaS platform, Tevico , offers intelligent cloud governance and observability for AWS workloads. It helps enterprises improve uptime, reduce costs, and ensure compliance by proactively detecting anomalies and triggering automated remediation workflows. As an AWS Advanced Consulting Partner, Comprinno helps organizations migrate, modernize, secure, and operate cloud environments while adopting emerging technologies such as Generative AI and Agentic AI. About the Role We are seeking an experienced Lead Data & AI Engineer to lead the design, implementation, and delivery of modern Data, Analytics, Machine Learning, and Generative AI solutions for enterprise customers. This role combines expertise in Data Engineering, Machine Learning, MLOps, Generative AI, and Cloud-Native Analytics. The ideal candidate will architect scalable data platforms, enable AI-ready data foundations, and lead the development of intelligent applications leveraging machine learning, Large Language Models (LLMs), and Generative AI frameworks. As a technical leader within Comprinno's Data Analytics & AI Practice, you will work closely with customers, Solution Architects, Data Scientists, Cloud Engineers, and business stakeholders to deliver innovative solutions that generate measurable business outcomes. This role also involves mentoring engineers, contributing to reusable frameworks and accelerators, supporting presales initiatives, and shaping the future direction of Comprinno's Data & AI offerings. Key Responsibilities Data Engineering & Analytics • Lead the design and implementation of scalable data platforms, pipelines, and analytics solutions. • Build and optimize data lakes, lakehouses, warehouses, and real-time data processing systems. • Design and implement robust ETL/ELT workflows for structured and unstructured data. • Establish data engineering best practices for performance, scalability, reliability, and security. • Ensure data quality, governance, lineage, observability, and compliance across customer environments. • Design modern architectures including: • Data Lakes • Data Lakehouses • Data Warehouses • Real-Time Streaming Platforms • Data Mesh Architectures Machine Learning & Applied AI • Design, develop, and operationalize machine learning solutions for enterprise use cases. • Build predictive, prescriptive, and recommendation systems using modern ML frameworks. • Lead feature engineering, model training, evaluation, deployment, and optimization activities. • Collaborate with business stakeholders to identify AI and analytics opportunities. • Enable scalable AI and analytics solutions that drive measurable business outcomes. Generative AI & Agentic AI • Design and implement Generative AI applications using Large Language Models (LLMs). • Build Retrieval-Augmented Generation (RAG) systems, AI assistants, copilots, and knowledge discovery platforms. • Develop prompt engineering strategies, evaluation frameworks, and AI orchestration workflows. • Work with modern GenAI frameworks including: • LangChain • LangGraph • CrewAI • Hugging Face • OpenAI APIs • Anthropic APIs • Explore and implement Agentic AI architectures and intelligent automation solutions. • Evaluate foundation models and recommend optimal architectures for business use cases. AWS Data & AI Solutions • Architect and implement cloud-native Data & AI solutions on AWS. • Work extensively with: • Amazon SageMaker • Amazon Bedrock • AWS Glue • Amazon Athena • Amazon Redshift • Amazon EMR • Amazon Kinesis • AWS Lake Formation • Amazon OpenSearch • SageMaker Data Wrangler • Build scalable, secure, and production-ready Data & AI platforms. MLOps & AI Platform Engineering • Design and implement MLOps frameworks for model lifecycle management. • Build automated pipelines for training, deployment, monitoring, retraining, and governance. • Implement AI observability, model monitoring, evaluation, and compliance controls. • Establish reusable AI deployment pipelines and engineering accelerators. • Collaborate with CloudOps and DevOps teams to operationalize AI workloads. Customer Engagement & Solutioning • Lead customer discovery workshops and AI strategy discussions. • Translate business requirements into scalable Data & AI architectures. • Present architecture recommendations, roadmaps, and technical proposals to customer stakeholders. • Support presales, effort estimation, solution validation, and proposal development activities. • Act as a trusted advisor to customers throughout the engagement lifecycle. Leadership & Mentorship • Mentor Data & AI Engineers and guide technical development across the team. • Conduct architecture reviews, code reviews, and design reviews. • Define engineering standards and best practices for the Data & AI practice. • Contribute to hiring, onboarding, and capability development initiatives. • Drive innovation through reusable frameworks, accelerators, and intellectual property creation. Required Qualifications & Skills • Bachelor's or Master's degree in: • Computer Science • Data Engineering • Data Science • Artificial Intelligence • Statistics • Mathematics • Related technical disciplines • 6–9 years of experience in Data Engineering, Machine Learning, Applied AI, Analytics, or Cloud Data Platforms. • At least 2 years of experience leading teams or mentoring engineers. • Strong programming expertise in Python and SQL. • Deep expertise in: • Apache Spark • Kafka • Airflow • Data Warehousing • Data Modeling • ETL/ELT Design • Experience building and managing large-scale data platforms and pipelines. • Strong understanding of: • Data Lakes • Lake