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This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a AI Engineer – Python & Snowflake based in India.
This role sits at the intersection of data engineering and applied artificial intelligence, focused on building scalable, production-ready AI solutions powered by modern cloud data platforms. You will design and operationalize AI-driven systems that transform large-scale structured and unstructured data into intelligent products and services. The position involves working across Snowflake environments, Python-based backend systems, and advanced AI/ML frameworks, including LLM and Generative AI technologies. You will collaborate closely with data scientists, ML engineers, and software engineers to bring models into production using robust MLOps and LLMOps practices. The role also includes building APIs, microservices, and data pipelines that enable enterprise-wide access to AI capabilities. This is a high-impact opportunity in an innovation-driven environment where experimentation, scalability, and production excellence are key priorities.
Accountabilities
- Design, build, and maintain AI-powered data platforms, pipelines, and data products supporting advanced analytics and machine learning use cases.
- Develop scalable backend systems and data workflows using Python and Snowflake, ensuring performance and reliability.
- Prepare, transform, and manage large-scale structured and unstructured datasets for AI, ML, and Generative AI applications.
- Build and optimize feature engineering frameworks, embeddings, vectorized datasets, and semantic data models.
- Deploy, monitor, and maintain AI/ML models in production environments, ensuring stability, scalability, and observability.
- Develop APIs, microservices, and backend services to expose AI capabilities across enterprise systems.
- Collaborate with cross-functional teams to operationalize AI solutions using MLOps and LLMOps best practices.
- Optimize Snowflake architecture for performance, cost efficiency, and large-scale AI workloads.
- Support Retrieval-Augmented Generation (RAG) pipelines and other Generative AI architectures.
- Evaluate and implement emerging AI technologies, including LLMs, vector databases, AI agents, and automation frameworks.
- Contribute to architectural decisions and technical strategy for AI-driven systems and platforms.
- Ensure strong standards of data quality, governance, security, and system observability across solutions.
Requirements
The ideal candidate brings 3–7 years of experience in AI engineering, data engineering, or machine learning engineering, with strong hands-on expertise in building production-grade systems. You should be highly proficient in Python and experienced in Snowflake-based data solutions, with strong SQL and data modeling capabilities.
- Strong experience in Python for building scalable applications, data pipelines, and backend services.
- Hands-on expertise with Snowflake, including data modeling, performance optimization, and architecture design.
- Advanced SQL skills and solid understanding of data architecture and database design principles.
- Experience building ETL/ELT pipelines in cloud-based environments.
- Knowledge of machine learning concepts, feature engineering, and model lifecycle management.
- Experience deploying and monitoring ML models in production environments.
- Ability to build APIs, microservices, and integrate enterprise systems.
- Familiarity with cloud platforms such as AWS, Azure, or GCP.
- Strong analytical and problem-solving skills with the ability to work independently.
- Exposure to LLMs, RAG architectures, vector databases, or AI agent frameworks is highly desirable.
- Experience with MLOps/LLMOps practices and orchestration tools (e.g., Airflow, Prefect) is a plus.
- Strong collaboration and communication skills in cross-functional environments.
Benefits
- Competitive compensation aligned with market standards
- Opportunity to work on cutting-edge AI, LLM, and Generative AI systems
- Exposure to large-scale enterprise data platforms and Snowflake ecosystems
- Flexible and collaborative work environment
- Professional development and continuous learning opportunities
- Career growth in a global technology and consulting organization
- Participation in high-impact, innovation-driven AI projects
- Inclusive, diverse, and knowledge-sharing culture
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