Job description
Duties & Responsibilities
- Design, build, and ship GenAI-powered solutions using frameworks like Lang. Chain, Llama. Index, Hugging Face, and Azure AI Studio.
- Develop and operationalize agentic workflows for autonomous, multi-step task execution using Lang. Graph, OpenAI Agents SDK, Auto. Gen/CrewAI, and Databricks Genie.
- Partner with cross-functional teams (data science, product, and business) to translate requirements into production-grade AI applications with measurable business impact.
- Build and maintain robust data pipelines, RAG pipelines, and model deployment workflows using Databricks, Snowflake, and Azure Data Factory.
- Own prompt engineering, model evaluation, and fine-tuning of LLMs for enterprise use cases, including offline and online evaluation pipelines.
- Implement responsible AI practices through observability, guardrails, governance, and feedback loops.
- Contribute reusable components, libraries, and best practices that accelerate the team's GenAI delivery.
Requirements
Minimum Years of Experience Description 3+ years - 2 to 4 years of AI/ML experience, including hands-on delivery on Databricks 3+ years - 3+ years of experience with Python, SQL, Py. Spark and one or more Gen AI frameworks such as Langgraph, OpenAI Agents SDK, Autogen, CrewAI, etc.
Basic Qualifications
- Bachelor's degree in Computer Science, Engineering, or related field.
- 2-4 years of experience in AI/ML engineering, with hands-on delivery of GenAI and agentic architectures.
- Proven experience deploying enterprise-grade AI solutions and integrating them into business workflows.
- Solid understanding of NLP, transformer models, RAG patterns, and MLOps practices.
- Experience with AI-native software development practices and tools (e.g., GitHub Copilot, AI-assisted SDLC).
Preferred Qualifications
- Experience in Test Driven Development and model evaluation strategies, including offline and online evaluation pipelines.
- Prior work experience in an agile, cross-functional team, collaborating with data scientists, engineers, and product managers.
- Ability to break down complex AI problems, estimate development effort, and deliver scalable solutions independently.
- Up-to-date knowledge of GenAI and LLM trends, including frameworks like Gemini, Llama. Index, and Lang. Graph, and their enterprise applications.
- Understanding of AI governance, including model explainability, fairness, and security (e.g., prompt injection, data leakage mitigation).
- Exposure to cloud platforms (Azure preferred) and containerized deployment (Docker, Kubernetes/AKS).