Senior AI Engineer

• 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

• Master’s 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.

*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.

We are looking for a senior AI engineering leader with strong expertise in LLMs, RAG, and AI orchestration frameworks, capable of building scalable enterprise AI solutions, leading teams, and translating business needs into impactful AI systems.