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We are looking for an experienced AI Engineer to design, build, and operationalize AI driven solutions for our global Analytics organization. The ideal candidate will have strong hands-on expertise in Python, PySpark, agentic workflow development, and modern GenAI frameworks, with experience building scalable applications using LLMs, retrieval systems, and automation pipelines.
Key Responsibilities:
- Agent Development & Architecture
- Build agentic workflows using LangChain/LangGraph and similar frameworks.
- Develop autonomous agents for data validation, reporting, document processing, and domain workflows.
- Deploy scalable, resilient agent pipelines with monitoring and evaluation.
- GenAI Application Engineering
- Develop GenAI applications using models like GPT, Gemini, and LLaMA.
- Implement RAG, vector search, prompt orchestration, and model evaluation.
- Partner with data scientists to productionize POCs.
- Data & Platform Engineering
- Build distributed data pipelines (Python, PySpark).
- Develop APIs, SDKs, and integration layers for AI-powered applications.
- Optimize systems for performance and scalability across cloud/hybrid environments.
- MLOps / LLMOps
- Contribute to CI/CD workflows for AI models—deployment, testing, monitoring.
- Implement governance, guardrails, and reusable GenAI frameworks.
- Collaboration & Stakeholder Engagement
- Work with analytics, product, and engineering teams to define and deliver AI solutions.
- Participate in architecture reviews and iterative development cycles.
- Support knowledge sharing and internal GenAI capability building.
Key Skills:
- 5–8 years of experience in AI/ML engineering, data science, or software engineering, with atleast 2 years focused on GenAI.
- Strong programming expertise in Python, distributed computing using PySpark, and API development.
- Hands on experience with LLM frameworks (LangChain, LangGraph, Transformers, OpenAI/Vertex/Bedrock SDKs).
- Experience developing AI agents, retrieval pipelines, tool calling structures, or autonomous task orchestration.
- Solid understanding of GenAI concepts: prompting, embeddings, RAG, evaluation metrics, hallucination identification, model selection, fine tuning, context engineering.