Job Description: AI Engineer (12-15 Years Experience) Position Title
Senior AI Engineer / Lead AI Engineer
Experience
12-15 Years
Location
Flexible / Hybrid / Onsite
Job Summary
We 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.
Key Responsibilities AI & Generative AI Development
- 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.
Large Language Models (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.
Natural Language Processing (NLP)
- Build advanced NLP solutions including:
- Text Classification
- Sentiment Analysis
- Named Entity Recognition (NER)
- Text Summarization
- Question Answering Systems
- Semantic Search
- Develop multilingual AI solutions.
Data Engineering & AI Data Pipelines
- 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.
MLOps & AI Operations
- 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.
Architecture & Leadership
- 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.
Required Technical Skills AI & Machine Learning
- Machine Learning
- Deep Learning
- Neural Networks
- Generative AI
- Agentic AI
- Reinforcement Learning
- Computer Vision (Good to Have)
LLM & GenAI Frameworks
- LangChain
- LangGraph
- LlamaIndex
- Semantic Kernel
- AutoGen
- CrewAI
- Haystack
Programming Languages
- Python (Expert)
- SQL
- PySpark
- Java/Scala (Preferred)
NLP Technologies
- Transformers
- Hugging Face
- BERT
- SpaCy
- NLTK
Data Engineering
- Databricks
- Apache Spark
- Delta Lake
- Azure Data Factory
- Snowflake
- Data Warehousing Concepts
Cloud Platforms
- Microsoft Azure
- AWS
- Google Cloud Platform (Preferred)
MLOps & DevOps
- MLflow
- Kubeflow
- Docker
- Kubernetes
- Git
- Azure DevOps
- GitHub Actions
Vector Databases
- Pinecone
- Weaviate
- FAISS
- ChromaDB
- Azure AI Search
Mandatory Qualifications
- 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.
Preferred Certifications
- 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
Soft Skills
- 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.
Key Deliverables
- 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.
Job Description: AI Engineer (12-15 Years Experience) Position Title
Senior AI Engineer / Lead AI Engineer
Experience
12-15 Years
Location
Flexible / Hybrid / Onsite
Job Summary
We 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.
Key Responsibilities AI & Generative AI Development
- 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.
Large Language Models (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.
Natural Language Processing (NLP)
- Build advanced NLP solutions including:
- Text Classification
- Sentiment Analysis
- Named Entity Recognition (NER)
- Text Summarization
- Question Answering Systems
- Semantic Search
- Develop multilingual AI solutions.
Data Engineering & AI Data Pipelines
- 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.
MLOps & AI Operations
- 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.
Architecture & Leadership
- 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.
Required Technical Skills AI & Machine Learning
- Machine Learning
- Deep Learning
- Neural Networks
- Generative AI
- Agentic AI
- Reinforcement Learning
- Computer Vision (Good to Have)
LLM & GenAI Frameworks
- LangChain
- LangGraph
- LlamaIndex
- Semantic Kernel
- AutoGen
- CrewAI
- Haystack
Programming Languages
- Python (Expert)
- SQL
- PySpark
- Java/Scala (Preferred)
NLP Technologies
- Transformers
- Hugging Face
- BERT
- SpaCy
- NLTK
Data Engineering
- Databricks
- Apache Spark
- Delta Lake
- Azure Data Factory
- Snowflake
- Data Warehousing Concepts
Cloud Platforms
- Microsoft Azure
- AWS
- Google Cloud Platform (Preferred)
MLOps & DevOps
- MLflow
- Kubeflow
- Docker
- Kubernetes
- Git
- Azure DevOps
- GitHub Actions
Vector Databases
- Pinecone
- Weaviate
- FAISS
- ChromaDB
- Azure AI Search
Mandatory Qualifications
- 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.
Preferred Certifications
- 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
Soft Skills
- 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.
Key Deliverables
- 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.
Job Description: AI Engineer (12-15 Years Experience) Position Title
Senior AI Engineer / Lead AI Engineer
Experience
12-15 Years
Location
Flexible / Hybrid / Onsite
Job Summary
We 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.
Key Responsibilities AI & Generative AI Development
- 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.
Large Language Models (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.
Natural Language Processing (NLP)
- Build advanced NLP solutions including:
- Text Classification
- Sentiment Analysis
- Named Entity Recognition (NER)
- Text Summarization
- Question Answering Systems
- Semantic Search
- Develop multilingual AI solutions.
Data Engineering & AI Data Pipelines
- 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.
MLOps & AI Operations
- 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.
Architecture & Leadership
- 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.
Required Technical Skills AI & Machine Learning
- Machine Learning
- Deep Learning
- Neural Networks
- Generative AI
- Agentic AI
- Reinforcement Learning
- Computer Vision (Good to Have)
LLM & GenAI Frameworks
- LangChain
- LangGraph
- LlamaIndex
- Semantic Kernel
- AutoGen
- CrewAI
- Haystack
Programming Languages
- Python (Expert)
- SQL
- PySpark
- Java/Scala (Preferred)
NLP Technologies
- Transformers
- Hugging Face
- BERT
- SpaCy
- NLTK
Data Engineering
- Databricks
- Apache Spark
- Delta Lake
- Azure Data Factory
- Snowflake
- Data Warehousing Concepts
Cloud Platforms
- Microsoft Azure
- AWS
- Google Cloud Platform (Preferred)
MLOps & DevOps
- MLflow
- Kubeflow
- Docker
- Kubernetes
- Git
- Azure DevOps
- GitHub Actions
Vector Databases
- Pinecone
- Weaviate
- FAISS
- ChromaDB
- Azure AI Search
Mandatory Qualifications
- 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.
Preferred Certifications
- 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
Soft Skills
- 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.
Key Deliverables
- 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.