Senior AI Engineer

Role Overview

We are seeking a highly skilled and versatile Senior AI Engineer to lead the organizational capability for design development and deployment of next-generation AI solutions that leverage Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) and modern orchestration frameworks.

The ideal candidate will have a proven track record in building advanced AI architectures leading technical teams and delivering scalable solutions that drive business impact. Beyond strong technical expertise this role requires the ability to collaborate with sales and pre-sales teams identify client opportunities and translate business requirements into high-performing AI systems and products. The role also involves building and institutionalizing AI capabilities across the organization establishing standards and best practices and overseeing execution to ensure successful delivery and sustainable adoption.

Key Responsibilities

Leadership & Solution Ownership

• Lead the design and implementation of AI solutions across multiple domains using LLMs RAG and intelligent agent architectures.
  
• Build and mentor AI engineering teams ensuring technical excellence and continuous innovation.
  
• Collaborate with business data and product teams to define and deliver end-to-end AI-driven solutions.
  
• Drive architectural decisions to ensure scalability reliability and maintainability of deployed AI systems.
  
• Guide the adoption of emerging tools and frameworks such as AWS Bedrock Mistral AI LLAMA and other cutting-edge AI ecosystems.
  
• Partner with sales and pre-sales teams to conceptualize present and deliver AI-based solutions for client opportunities proposals and demos.
  
• Participate in proof-of-concept (POC) and prototype development to validate ideas and demonstrate business value.
  
• Build organizational AI capability by defining frameworks standards and reusable components for scalable AI development.
  
• Oversee execution of AI initiatives across teams ensuring alignment with strategic objectives governance and delivery excellence.
  
• Participate in the training and development of junior engineers and organize structured training programs knowledge-sharing sessions and mentorship initiatives to build internal AI expertise.
  

Core Engineering Responsibilities

• LLM Development: Design fine-tune and deploy Large Language Models (LLMs) tailored to specific business domains and tasks.
  
• Instruction Fine-Tuning: Implement instruction fine-tuning and optimization strategies to enhance accuracy and contextual relevance.
  
• RAG Systems: Architect and develop Retrieval-Augmented Generation (RAG) including Agentic RAG and multi-agent architectures to improve response quality and reasoning.
  
• MCP Integration: Implement Model Context Protocol (MCP) for intelligent model coordination and context-sharing across systems.
  
• Data Preparation & Pipelines: Oversee data collection cleaning and transformation pipelines ensuring structured high-quality datasets for training and inference.
  
• Knowledge Graphs: Design and integrate graph databases and knowledge graph structures to enhance contextual reasoning and semantic search.
  
• Automation & Deployment: Build and automate scalable AI pipelines using Python PyTorch TensorFlow and cloud-native services (AWS Azure GCP).
  
• Governance & Security: Ensure compliance governance and ethical AI standards including data privacy access control and responsible model usage.
  
• Monitoring & Optimization: Continuously monitor model performance latency and reliability; optimize infrastructure for cost and scale.
  
• Execution Oversight: Establish delivery frameworks and quality controls to ensure projects are executed on time within scope and to technical standards.
  

Required Skills & Experience

• 5 years of experience in AI/ML development with at least 3 years in a senior or leadership role.
  
• Proven expertise in LLMs and Natural Language Processing (NLP) systems.
  
• Strong experience in Retrieval-Augmented Generation (RAG) Agentic RAG and multi-agent architectures.
  
• Hands-on experience with MCP (Model Context Protocol) and AI orchestration frameworks.
  
• Proficiency in Python PyTorch TensorFlow and related AI/ML libraries.
  
• Deep understanding of data preparation feature engineering and model training pipelines.
  
• Experience with graph databases (Neo4j Cosmos DB Graph) and knowledge graph development.
  
• Familiarity with AWS Bedrock Mistral AI LLAMA and other open-source AI models.
  
• Expertise in designing scalable secure and cost-efficient AI architectures across cloud environments (AWS Azure GCP).
  
• Strong leadership and communication skills with the ability to build and lead high-performing teams.
  
• Proven ability to collaborate with sales teams and translate client requirements into AI-powered business solutions.
  
• Demonstrated success in delivering enterprise-grade AI systems from concept to deployment.
  
• Proven experience in building organizational AI capability and leading the execution of strategic AI programs.
  
• Experience in mentoring junior engineers and organizing structured training programs to uplift team-wide AI skills and capability.
  

Preferred Qualifications

• Masters or Ph.D. in Computer Science Artificial Intelligence Machine Learning or a related field.
  
• Certifications in AI/ML AWS AI Specialty or Azure AI Engineer Associate.
  
• Experience working in regulated industries (e.g. healthcare finance or defense).
  
• Exposure to MLOps practices and tools for continuous training and deployment of AI models.
  
• Experience establishing organizational frameworks governance models and execution structures for enterprise-wide AI adoption.