5 days ago
Base Salary
$91k - $140k/yr
Responsibilities
- Design, develop, deploy, maintain, and improve enterprise-scale AI, Generative AI, and AI infrastructure solutions.
- Build LLM-powered applications, intelligent agents, multi-agent systems, conversational assistants, copilots, and knowledge-management solutions.
- Implement RAG, semantic search, vector-embedding, data-ingestion, transformation, and feature-engineering pipelines.
- Fine-tune, evaluate, optimize, deploy, and monitor commercial and open-source LLMs and AI models.
- Integrate AI applications with enterprise systems through APIs, microservices, and cloud-native architectures.
- Collaborate with data, product, engineering, and business stakeholders to translate requirements into scalable AI architectures.
- Address infrastructure issues and deliver AI infrastructure projects on schedule and within budget.
- Apply model-performance, safety, governance, security, and responsible-AI practices.
Requirements
- Bachelor's or master's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related field.
- At least 7 years of experience in Data Engineering, AI/ML, or related domains; the posting also states 9–12 years and 6–7 years in separate sections.
- Strong Python and SQL skills, with experience in ETL/ELT pipelines, data processing, data modeling, data lakes, lakehouses, and data warehousing.
- Hands-on experience with Databricks, Spark/PySpark, Azure Data Factory, or equivalent data platforms, plus Azure, AWS, or GCP.
- Production experience with Generative AI, Agentic AI, LLMs, RAG, prompt engineering, fine-tuning, semantic search, vector embeddings, model evaluation, quantization, model optimization, and LLMOps.
- Experience developing AI agents with LangChain, LangGraph, CrewAI, AutoGen, or Semantic Kernel.
- Strong NLP knowledge covering text classification, named entity recognition, topic modeling, sentiment analysis, text summarization, document intelligence, and information extraction.
- Experience with Hugging Face, spaCy, NLTK, Transformers, and commercial or open-source models including Claude, GPT-4/GPT-4o, Llama, Mistral, Gemini, and Falcon.
- Experience with vector databases such as Pinecone, ChromaDB, FAISS, Weaviate, or Azure AI Search.
- Knowledge of MLOps/LLMOps, MLflow, Azure ML, Kubeflow, Docker, Kubernetes, REST APIs, FastAPI, and Flask.
- Preferred certifications include Microsoft Azure AI Engineer Associate, Databricks Certified Data Engineer, and AWS Certified Machine Learning Specialty.
- Preferred experience includes enterprise copilots, AI assistants, regulated industries, knowledge graphs, Graph RAG, responsible AI, governance, and security practices.
Benefits
- Remote, full-time employment.
- The posting does not specify additional benefits or compensation.
Tech Stack
Apache SparkAWSAzureDatabricksDockerFastAPIFlaskGoogle Cloud PlatformKubernetesMLflowNLTKPythonspaCySQL
Categories
About EXL
EXL provides data analytics, AI, and digital operations services for enterprises in insurance, healthcare, banking, and other regulated industries. It delivers consulting, managed services, and technology platforms to improve decision-making, risk management, and operational efficiency. Founded in 1999 and headquartered in New York, EXL is a public company listed on the NASDAQ (ticker: EXLS).