Senior AI Engineer / Lead AI Engineer
Experience12-15 Years
LocationFlexible / Hybrid / Onsite
Job SummaryWe are seeking an experienced AI Engineer with 12-15 years of overall experience in Data Engineering, Artificial Intelligence, Machine Learning, Generative AI, and Agentic AI solutions. The ideal candidate will have a strong background in designing, developing, and deploying scalable AI solutions leveraging Large Language Models (LLMs), NLP, Deep Learning, and cloud-based AI platforms. This role requires a blend of technical leadership, solution architecture, stakeholder management, and hands-on development expertise.
- Design and develop enterprise-grade AI and Generative AI applications.
- Build and deploy AI-powered assistants, copilots, chatbots, and intelligent automation solutions.
- Develop Agentic AI frameworks capable of autonomous reasoning, planning, and task execution.
- Implement Retrieval Augmented Generation (RAG) architectures for enterprise knowledge management.
- Fine-tune, evaluate, and optimize foundation models and LLMs.
- Work with commercial and open-source LLMs including:
- Claude
- GPT Series
- Llama
- Mistral
- Gemma
- Falcon
- Develop prompt engineering strategies and model evaluation frameworks.
- Optimize inference performance, latency, and cost for production deployments.
- Build advanced NLP solutions including:
- Text Classification
- Sentiment Analysis
- Named Entity Recognition (NER)
- Text Summarization
- Question Answering Systems
- Semantic Search
- Develop multilingual AI solutions.
- Design scalable data ingestion, transformation, and feature engineering pipelines.
- Build AI-ready data platforms using structured and unstructured data.
- Integrate data from multiple enterprise systems and external sources.
- Ensure data quality, governance, security, and compliance.
- Develop CI/CD pipelines for AI and ML workloads.
- Implement model monitoring, observability, retraining, and governance frameworks.
- Automate AI model deployment and lifecycle management.
- Establish best practices for AI reliability, explainability, and responsible AI.
- Lead AI solution architecture discussions and design reviews.
- Mentor Data Scientists, ML Engineers, and AI Developers.
- Collaborate with business stakeholders to identify AI use cases and deliver measurable business value.
- Drive AI innovation initiatives and proof-of-concepts.
- Machine Learning
- Deep Learning
- Neural Networks
- Generative AI
- Agentic AI
- Reinforcement Learning
- Computer Vision (Good to Have)
- LangChain
- LangGraph
- LlamaIndex
- Semantic Kernel
- AutoGen
- CrewAI
- Haystack
- Python (Expert)
- SQL
- PySpark
- Java/Scala (Preferred)
- Transformers
- Hugging Face
- BERT
- SpaCy
- NLTK
- Databricks
- Apache Spark
- Delta Lake
- Azure Data Factory
- Snowflake
- Data Warehousing Concepts
- Microsoft Azure
- AWS
- Google Cloud Platform (Preferred)
- MLflow
- Kubeflow
- Docker
- Kubernetes
- Git
- Azure DevOps
- GitHub Actions
- Pinecone
- Weaviate
- FAISS
- ChromaDB
- Azure AI Search
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field.
- 12-15 years of overall IT experience.
- Minimum 5+ years in AI/ML development.
- Minimum 3+ years of hands-on experience in Generative AI and Large Language Models.
- Experience delivering AI solutions in production environments.
- Strong expertise in Python, Data Engineering, and Cloud Platforms.
- Microsoft Certified: Azure AI Engineer Associate (AI-102)
- Microsoft Certified: Azure Data Engineer Associate (DP-203)
- Databricks Generative AI Certification
- AWS Certified Machine Learning Specialty
- Google Professional Machine Learning Engineer
- Strong communication and presentation skills.
- Excellent stakeholder management capabilities.
- Ability to translate business requirements into AI solutions.
- Team leadership and mentoring experience.
- Strategic thinking and problem-solving skills.
- Enterprise AI Solutions
- Generative AI Applications
- Agentic AI Frameworks
- RAG-Based Knowledge Systems
- LLM-Powered Business Applications
- AI Governance & MLOps Frameworks
- Production-Ready Machine Learning Solutions
This role is ideal for a seasoned AI professional capable of leading enterprise-wide AI transformation initiatives while remaining hands-on with modern Generative AI, Agentic AI, NLP, Data Engineering, and LLM technologies.
ResponsibilitiesJob Description: AI Engineer (12-15 Years Experience) Position TitleSenior AI Engineer / Lead AI Engineer
Experience12-15 Years
LocationFlexible / Hybrid / Onsite
Job SummaryWe are seeking an experienced AI Engineer with 12-15 years of overall experience in Data Engineering, Artificial Intelligence, Machine Learning, Generative AI, and Agentic AI solutions. The ideal candidate will have a strong background in designing, developing, and deploying scalable AI solutions leveraging Large Language Models (LLMs), NLP, Deep Learning, and cloud-based AI platforms. This role requires a blend of technical leadership, solution architecture, stakeholder management, and hands-on development expertise.
- Design and develop enterprise-grade AI and Generative AI applications.
- Build and deploy AI-powered assistants, copilots, chatbots, and intelligent automation solutions.
- Develop Agentic AI frameworks capable of autonomous reasoning, planning, and task execution.
- Implement Retrieval Augmented Generation (RAG) architectures for enterprise knowledge management.
- Fine-tune, evaluate, and optimize foundation models and LLMs.
- Work with commercial and open-source LLMs including:
- Claude
- GPT Series
- Llama
- Mistral
- Gemma
- Falcon
- Develop prompt engineering strategies and model evaluation frameworks.
- Optimize inference performance, latency, and cost for production deployments.
- Build advanced NLP solutions including:
- Text Classification
- Sentiment Analysis
- Named Entity Recognition (NER)
- Text Summarization
- Question Answering Systems
- Semantic Search
- Develop multilingual AI solutions.
- Design scalable data ingestion, transformation, and feature engineering pipelines.
- Build AI-ready data platforms using structured and unstructured data.
- Integrate data from multiple enterprise systems and external sources.
- Ensure data quality, governance, security, and compliance.
- Develop CI/CD pipelines for AI and ML workloads.
- Implement model monitoring, observability, retraining, and governance frameworks.
- Automate AI model deployment and lifecycle management.
- Establish best practices for AI reliability, explainability, and responsible AI.
- Lead AI solution architecture discussions and design reviews.
- Mentor Data Scientists, ML Engineers, and AI Developers.
- Collaborate with business stakeholders to identify AI use cases and deliver measurable business value.
- Drive AI innovation initiatives and proof-of-concepts.
- Machine Learning
- Deep Learning
- Neural Networks
- Generative AI
- Agentic AI
- Reinforcement Learning
- Computer Vision (Good to Have)
- LangChain
- LangGraph
- LlamaIndex
- Semantic Kernel
- AutoGen
- CrewAI
- Haystack
- Python (Expert)
- SQL
- PySpark
- Java/Scala (Preferred)
- Transformers
- Hugging Face
- BERT
- SpaCy
- NLTK
- Databricks
- Apache Spark
- Delta Lake
- Azure Data Factory
- Snowflake
- Data Warehousing Concepts
- Microsoft Azure
- AWS
- Google Cloud Platform (Preferred)
- MLflow
- Kubeflow
- Docker
- Kubernetes
- Git
- Azure DevOps
- GitHub Actions
- Pinecone
- Weaviate
- FAISS
- ChromaDB
- Azure AI Search
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field.
- 12-15 years of overall IT experience.
- Minimum 5+ years in AI/ML development.
- Minimum 3+ years of hands-on experience in Generative AI and Large Language Models.
- Experience delivering AI solutions in production environments.
- Strong expertise in Python, Data Engineering, and Cloud Platforms.
- Microsoft Certified: Azure AI Engineer Associate (AI-102)
- Microsoft Certified: Azure Data Engineer Associate (DP-203)
- Databricks Generative AI Certification
- AWS Certified Machine Learning Specialty
- Google Professional Machine Learning Engineer
- Strong communication and presentation skills.
- Excellent stakeholder management capabilities.
- Ability to translate business requirements into AI solutions.
- Team leadership and mentoring experience.
- Strategic thinking and problem-solving skills.
- Enterprise AI Solutions
- Generative AI Applications
- Agentic AI Frameworks
- RAG-Based Knowledge Systems
- LLM-Powered Business Applications
- AI Governance & MLOps Frameworks
- Production-Ready Machine Learning Solutions
This role is ideal for a seasoned AI professional capable of leading enterprise-wide AI transformation initiatives while remaining hands-on with modern Generative AI, Agentic AI, NLP, Data Engineering, and LLM technologies.
QualificationsJob Description: AI Engineer (12-15 Years Experience) Position TitleSenior AI Engineer / Lead AI Engineer
Experience12-15 Years
LocationFlexible / Hybrid / Onsite
Job SummaryWe are seeking an experienced AI Engineer with 12-15 years of overall experience in Data Engineering, Artificial Intelligence, Machine Learning, Generative AI, and Agentic AI solutions. The ideal candidate will have a strong background in designing, developing, and deploying scalable AI solutions leveraging Large Language Models (LLMs), NLP, Deep Learning, and cloud-based AI platforms. This role requires a blend of technical leadership, solution architecture, stakeholder management, and hands-on development expertise.
- Design and develop enterprise-grade AI and Generative AI applications.
- Build and deploy AI-powered assistants, copilots, chatbots, and intelligent automation solutions.
- Develop Agentic AI frameworks capable of autonomous reasoning, planning, and task execution.
- Implement Retrieval Augmented Generation (RAG) architectures for enterprise knowledge management.
- Fine-tune, evaluate, and optimize foundation models and LLMs.
- Work with commercial and open-source LLMs including:
- Claude
- GPT Series
- Llama
- Mistral
- Gemma
- Falcon
- Develop prompt engineering strategies and model evaluation frameworks.
- Optimize inference performance, latency, and cost for production deployments.
- Build advanced NLP solutions including:
- Text Classification
- Sentiment Analysis
- Named Entity Recognition (NER)
- Text Summarization
- Question Answering Systems
- Semantic Search
- Develop multilingual AI solutions.
- Design scalable data ingestion, transformation, and feature engineering pipelines.
- Build AI-ready data platforms using structured and unstructured data.
- Integrate data from multiple enterprise systems and external sources.
- Ensure data quality, governance, security, and compliance.
- Develop CI/CD pipelines for AI and ML workloads.
- Implement model monitoring, observability, retraining, and governance frameworks.
- Automate AI model deployment and lifecycle management.
- Establish best practices for AI reliability, explainability, and responsible AI.
- Lead AI solution architecture discussions and design reviews.
- Mentor Data Scientists, ML Engineers, and AI Developers.
- Collaborate with business stakeholders to identify AI use cases and deliver measurable business value.
- Drive AI innovation initiatives and proof-of-concepts.
- Machine Learning
- Deep Learning
- Neural Networks
- Generative AI
- Agentic AI
- Reinforcement Learning
- Computer Vision (Good to Have)
- LangChain
- LangGraph
- LlamaIndex
- Semantic Kernel
- AutoGen
- CrewAI
- Haystack
- Python (Expert)
- SQL
- PySpark
- Java/Scala (Preferred)
- Transformers
- Hugging Face
- BERT
- SpaCy
- NLTK
- Databricks
- Apache Spark
- Delta Lake
- Azure Data Factory
- Snowflake
- Data Warehousing Concepts
- Microsoft Azure
- AWS
- Google Cloud Platform (Preferred)
- MLflow
- Kubeflow
- Docker
- Kubernetes
- Git
- Azure DevOps
- GitHub Actions
- Pinecone
- Weaviate
- FAISS
- ChromaDB
- Azure AI Search
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field.
- 12-15 years of overall IT experience.
- Minimum 5+ years in AI/ML development.
- Minimum 3+ years of hands-on experience in Generative AI and Large Language Models.
- Experience delivering AI solutions in production environments.
- Strong expertise in Python, Data Engineering, and Cloud Platforms.
- Microsoft Certified: Azure AI Engineer Associate (AI-102)
- Microsoft Certified: Azure Data Engineer Associate (DP-203)
- Databricks Generative AI Certification
- AWS Certified Machine Learning Specialty
- Google Professional Machine Learning Engineer
- Strong communication and presentation skills.
- Excellent stakeholder management capabilities.
- Ability to translate business requirements into AI solutions.
- Team leadership and mentoring experience.
- Strategic thinking and problem-solving skills.
- Enterprise AI Solutions
- Generative AI Applications
- Agentic AI Frameworks
- RAG-Based Knowledge Systems
- LLM-Powered Business Applications
- AI Governance & MLOps Frameworks
- Production-Ready Machine Learning Solutions
This role is ideal for a seasoned AI professional capable of leading enterprise-wide AI transformation initiatives while remaining hands-on with modern Generative AI, Agentic AI, NLP, Data Engineering, and LLM technologies.
Skills Required
- Bachelor’s or master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field
- 12–15 years of overall IT experience
- At least 5 years of AI/ML development experience
- At least 3 years of hands-on Generative AI and Large Language Model experience
- Experience delivering AI solutions in production environments
- Strong expertise in Python, data engineering, and cloud platforms
- Microsoft Certified: Azure AI Engineer Associate (AI-102)
- Microsoft Certified: Azure Data Engineer Associate (DP-203)
- Databricks Generative AI Certification
- AWS Certified Machine Learning Specialty
- Google Professional Machine Learning Engineer certification
- Java or Scala experience
- Google Cloud Platform experience
- Computer Vision experience
- Strong communication and presentation skills
- Stakeholder management capabilities
- Team leadership and mentoring experience
- Strategic thinking and problem-solving skills
EXL Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about EXL and has not been reviewed or approved by EXL.
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Healthcare Strength — Medical plan options include HSA, HRA, and PPO choices, with additional health-related programs such as disability coverage and an employee assistance program. Feedback suggests these options provide meaningful flexibility and coverage breadth for many roles.
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Leave & Time Off Breadth — Up to 20 days of paid time off, plus federal holidays and paid sick and safe time, are highlighted. Feedback suggests this leave structure is a notable benefit alongside core compensation.
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Retirement Support — A traditional and Roth 401(k) with up to a 3% company match and full vesting after one year is described. Feedback suggests this match and vesting timeline help bolster long-term savings.
EXL Insights
What We Do
Choosing a digital partner is about more than capabilities — it’s about collaboration and character. Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments. At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations. Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale. Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact. We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition. At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward. For more information, visit www.exlservice.com.
