Senior AI / GenAI Engineer
Job Title
Senior AI / GenAI Engineer
Experience
5–12 Years
Job Type
Full-time
Role Overview
We are looking for an experienced AI / GenAI Engineer with 5–12 years of hands-on experience in designing, developing, and deploying enterprise-grade AI and machine learning solutions.
The ideal candidate will have strong expertise in AI, Machine Learning, Generative AI, LLMs, Agentic AI, Chatbots, RAG, Python, and Azure Databricks. The candidate should be capable of taking AI solutions from ideation and experimentation through production deployment, optimization, and ongoing monitoring.
The role requires a strong combination of AI engineering, software development, data engineering, LLM application development, and cloud technologies, with the ability to work on scalable enterprise AI solutions.
Primary Skills
- Artificial Intelligence (AI)
- Machine Learning (ML)
- Generative AI (GenAI)
- Large Language Models (LLMs)
- Agentic AI / AI Agents
- Retrieval-Augmented Generation (RAG)
- AI-powered Chatbots
- Python
- Azure Databricks
Key Responsibilities
AI & Machine Learning
- Design, develop, train, evaluate, and deploy machine learning models.
- Apply appropriate ML algorithms to solve complex business problems.
- Perform data preparation, feature engineering, model development, validation, and optimization.
- Build scalable AI/ML solutions for enterprise use cases.
- Evaluate model performance and continuously improve accuracy, scalability, and reliability.
Generative AI & LLM
- Design and implement Generative AI applications using LLMs.
- Work with commercial and open-source LLMs and integrate them into enterprise applications.
- Develop LLM-based solutions for text generation, summarization, classification, information extraction, question answering, and conversational AI.
- Implement prompt engineering and optimize prompts for accuracy, relevance, and consistency.
- Work with embeddings, tokenization, context management, model evaluation, and LLM observability.
- Evaluate different LLM models based on quality, latency, cost, security, and business requirements.
Agentic AI
- Design and develop Agentic AI systems and autonomous AI agents.
- Build multi-step AI workflows capable of reasoning, planning, tool usage, and task execution.
- Develop AI agents that interact with APIs, databases, enterprise systems, and external tools.
- Implement agent orchestration, memory, context management, guardrails, and human-in-the-loop workflows.
- Design reliable and scalable agentic architectures for enterprise use cases.
RAG & Knowledge-Based AI
- Design and implement Retrieval-Augmented Generation (RAG) solutions.
- Build document ingestion, chunking, embedding, indexing, retrieval, and generation pipelines.
- Implement semantic search, hybrid search, metadata filtering, and contextual retrieval.
- Work with vector databases and enterprise knowledge repositories.
- Improve RAG accuracy through retrieval optimization, reranking, prompt engineering, and evaluation.
- Implement mechanisms to reduce hallucinations and improve response relevance and grounding.
AI Chatbots
- Develop intelligent AI-powered chatbots and conversational applications.
- Build context-aware, multi-turn conversational experiences.
- Integrate LLMs, RAG, APIs, enterprise data, and business workflows into chatbots.
- Implement conversation history, user context, authentication, guardrails, and fallback mechanisms.
- Monitor chatbot quality, response accuracy, latency, and user experience.
Python & Software Engineering
- Develop production-quality AI/ML applications using Python.
- Build reusable, modular, scalable, and testable AI components.
- Develop REST APIs and integrate AI services with enterprise applications.
- Follow software engineering best practices including Git, testing, CI/CD, code reviews, logging, and documentation.
- Optimize applications for performance, scalability, reliability, and cost.
Azure Databricks
- Design and develop AI/ML and data processing solutions using Azure Databricks.
- Work with Databricks notebooks, clusters, Spark, Delta Lake, and data pipelines.
- Process and transform large-scale datasets for AI/ML workloads.
- Develop ML workflows and integrate Databricks with Azure services.
- Support model development, experimentation, deployment, and monitoring.
- Optimize Spark/Databricks workloads for performance and cost.
- Implement secure and scalable data and AI architectures on Azure.
Required Technical Skills
SkillExpected Proficiency
Python
Advanced
Artificial Intelligence
Advanced
Machine Learning
Advanced
Generative AI
Advanced
LLMs
Advanced
Agentic AI / AI Agents
Strong
RAG
Advanced
AI Chatbots
Strong
Azure Databricks
Strong
NLP
Strong
Prompt Engineering
Strong
Embeddings & Vector Search
Strong
REST APIs
Strong
SQL
Strong
Git
Strong
Preferred Technologies
Experience with some of the following technologies is desirable:
- LLM Platforms: Azure OpenAI, OpenAI, Hugging Face, open-source LLMs
- AI/ML Frameworks: PyTorch, TensorFlow, scikit-learn
- GenAI Frameworks: LangChain, LlamaIndex, Semantic Kernel or equivalent
- Agentic AI: Agent orchestration frameworks, tool/function calling, multi-agent workflows
- Data & Search: Apache Spark, Delta Lake, vector databases, Azure AI Search
- Cloud: Microsoft Azure
- DevOps: Docker, CI/CD, Azure DevOps/GitHub
- APIs: REST APIs, FastAPI
- MLOps: MLflow and model monitoring/evaluation frameworks
Experience Expectations
5–7 Years
- Strong hands-on experience in Python, AI/ML, and software development.
- Practical experience building and deploying GenAI/LLM applications.
- Experience with RAG and AI chatbot development.
- Working knowledge of Azure Databricks and cloud-based AI solutions.
- Ability to independently develop and productionize AI solutions.
8–10 Years
- Strong expertise in AI/ML and GenAI solution development.
- Experience designing scalable LLM, RAG, chatbot, and Agentic AI architectures.
- Strong Azure and Azure Databricks experience.
- Ability to lead technical implementation and mentor engineers.
- Experience taking AI solutions from POC to production.
10–12 Years
- Deep technical expertise in AI/ML, GenAI, LLM, RAG, and Agentic AI.
- Proven experience designing enterprise-scale AI architectures.
- Ability to define AI technical strategy and architecture.
- Strong experience with Azure Databricks and enterprise cloud environments.
- Experience leading AI engineering teams and complex AI transformation initiatives.
- Strong stakeholder management and ability to translate business requirements into scalable AI solutions.
Key Responsibilities at Senior Level
- Lead architecture and development of enterprise AI and GenAI solutions.
- Define technical approaches for LLM, RAG, chatbot, and Agentic AI use cases.
- Conduct technical evaluations and proof-of-concepts for emerging AI technologies.
- Establish best practices for prompt engineering, RAG, model evaluation, security, and responsible AI.
- Mentor junior and mid-level AI engineers.
- Collaborate with data scientists, data engineers, software engineers, architects, product managers, and business stakeholders.
- Ensure AI solutions meet requirements for scalability, security, performance, reliability, and cost.
- Stay current with rapidly evolving GenAI and Agentic AI technologies.
Education
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related discipline.
Soft Skills
- Strong analytical and problem-solving abilities.
- Excellent communication and collaboration skills.
- Strong ownership and ability to work independently.
- Ability to explain complex AI concepts to technical and non-technical stakeholders.
- Strong mentoring and leadership capabilities.
- Continuous learning mindset and passion for emerging AI technologies.
Keywords for Recruitment
AI Engineer, Senior AI Engineer, AI/ML Engineer, Machine Learning Engineer, GenAI Engineer, Generative AI Engineer, LLM Engineer, Agentic AI Engineer, RAG Engineer, AI Chatbot Developer, Python, Machine Learning, Artificial Intelligence, Generative AI, LLM, RAG, Agentic AI, AI Agents, Chatbots, Azure, Azure Databricks, PySpark, MLflow, Azure OpenAI, NLP, Prompt Engineering, Vector Database, Semantic Search, Azure AI Search.
