Global IT GenAI Senior Specialist

KEY RESPONSIBILITIES

1. Contribute to the design, development, and deployment of GenAI solutions, ensuring scalability and measurable business impact.
2. Act as a hands-on coding contributor (IC), driving AI/GenAI use cases from prototype to production.
3. Build and maintain scalable data ingestion pipelines for structured, semi-structured, and unstructured data.
4. Implement ETL/ELT workflows using dbt.
5. Ensure data quality and compliance with governance and security requirements across data ingestion processes.
6. Curate, transform, and prepare datasets for LLM fine-tuning, embeddings, and retrieval-augmented generation (RAG) pipelines.
7. Work with vector databases to optimize context retrieval for GenAI applications.
8. Contribute to MLOps/LLMOps practices, including CI/CD pipelines, performance monitoring, and guardrails to reduce risks like hallucinations.
9. Collaborate closely with other data engineers, AI engineers, and product owners to deliver robust, reliable solutions.
10. Share insights and stay up-to-date on emerging GenAI frameworks (LangChain, LlamaIndex, multi-agent systems).
11. Contribute knowledge to team discussions, helping evolve GenAI best practices within the portfolio.

What You'll Bring

AI/ML & LLM engineering skills

1. 5+ years in technology roles, with 4+ years in Data Engineering and 1+ years in GenAI/AI product development at scale.
2. Proven experience with Agentic AI architectures, multi-agent systems, or hands-on implementation of RAG pipelines.
3. Hands-on coding in Python and exposure to Gen AI frameworks such as LangChain, LlamaIndex etc.
4. Strong understanding of LLMOps/MLOps principles to deploy, monitor models, and implement CI/CD automation.
5. Experience in deploying solutions on a cloud platform – AWS (preferred), Azure/GCP with containerization (Docker/Kubernetes).
6. Demonstrate problem-solving skills

Data Ingestion Engineering Skills

1. Strong knowledge of Python, SQL, dbt and Snowflake for building and maintaining data ingestion pipelines.
2. Experience working with AWS Glue and S3
3. Hands-on experience working with structured, semi-structured and unstructured datasets
4. Ability to analyze performance bottlenecks and optimize data pipelines for efficiency and scalability.
5. Exposure to version control (GitHub/Bitbucket) and Agile methodologies.

Communication, interpersonal, and teaming skills

1. Good communication and stakeholder engagement skills to explain technical solutions in business language.
2. Experience working in a fast-paced global company, with a diverse team of both technical and functional backgrounds, located in multiple time zones