• Contribute to experimentation with novel ML and Generative AI architectures
• Create and maintain embedding models (domain-specific embeddings, document/text embeddings)
• Support Retrieval Augmented Generation (RAG) solutions, including semantic search, vector databases, and LLM integration
• Help design and implement AI powered enterprise applications
• Analyze large datasets to identify trends, patterns, and actionable insights
• Partner with AI/ML Engineers and Leads to enhance existing models and improve model accuracy and performance
• Optimize AI pipelines for scalability, performance, reliability, and cost efficiency
• Integrate AI/ML solutions into enterprise systems by partnering closely with data engineering, product, and platform teams
• Collaborate effectively with cross-functional teams and stakeholders, communicating complex technical concepts clearly
• Contribute to skill development for engineers and interns in AI/LLM techniques
• Comply with company policies, procedures, and directives
• Comply with all applicable Company policies, procedures, and business directives, changes including those relating to work location, team assignments, work schedules, and flexible work arrangements

Required Qualifications:

• Bachelor's/Master's in CS, Engineering, Mathematics/Statistics, or related
• 4+ years of relevant experience, including solid ML model development
• Hands-on expertise in Databricks, Spark, Python, Scala, SQL
• LLM & GenAI Skills:
  • Creating custom embedding models
  • Building RAG pipelines
  • Vector DBs & semantic search
  • Using LangChain for agentic workflows (tools, agents, memory)
  • Experience with OpenAI/Azure OpenAI APIs
  • Experience with Azure cloud for compute, security, and resource management
  • CI/CD familiarity using Git, GitHub Actions/Jenkins, Terraform
• Proven solid ability to build ML models end to end (data prep → feature creation → training → evaluation)
  • Proven excellent communication and partnership skills