Description

• Development and delivery of high-quality software solutions by using industry aligned programming languages, frameworks, and tools. Ensuring that code is scalable, maintainable, and optimized for performance.
• Cross-functional collaboration with product managers, designers, and other engineers to define software requirements, devise solution strategies, and ensure seamless integration and alignment with business objectives.
• Collaboration with peers, participate in code reviews, and promote a culture of code quality and knowledge sharing.
• Stay informed of industry technology trends and innovations and actively contribute to the organization’s technology communities to foster a culture of technical excellence and growth.
• Adherence to secure coding practices to mitigate vulnerabilities, protect sensitive data, and ensure secure software solutions.
• Implementation of effective unit testing practices to ensure proper code design, readability, and reliability.

Qualifications:

• Exp : 9+ years

be successful as a Senior AI Engineer, you should have experience with:

• Expert Python & AI Engineering Frameworks- Deep proficiency in Python and modern AI frameworks (e.g., LangChain, LangGraph, HuggingFace), including vector‐retrieval tooling.
• Agentic AI & Orchestrated Reasoning- Hands‐on experience designing and deploying agentic AI workflows, tool‐using agents, and multi‐step reasoning systems in production environments.
• RAG Architecture & Implementation- Practical experience designing and implementing Retrieval‐Augmented Generation (RAG) solutions, including embeddings, chunking, retrieval optimisation, and safety/guardrails.
• Production‐Grade AI Application Engineering- Proven ability to build and operate full‐stack AI applications (backend, APIs, modern front‐end frameworks such as React) with strong focus on reliability, scalability, security, and observability.
• Cloud‐Native AI Deployment on AWS- Experience deploying AI solutions using AWS services such as Bedrock, SageMaker, Lambda, API Gateway, and vector‐enabled datastores (e.g., OpenSearch, pgvector).

Some other highly valued skills may include:

• End‐to‐End MLOps / LLMOps- Experience with model lifecycle management, evaluation frameworks, monitoring, and CI/CD for AI workloads.
• Technical Leadership & Mentorship- Experience leading junior engineers, driving design reviews, and setting engineering best practices.
• Model Fine‐Tuning Expertise- Understanding of fine‐tuning techniques and when to apply fine‐tuning vs. RAG vs. hybrid strategies.
• Enterprise‐Grade Governance & Security- Experience designing AI systems within regulated or compliance‐heavy environments.
• Cost‐Optimised AI Architecture- Ability to design scalable, efficient AI systems through model selection, inference optimisation, and resource‐efficient deployment

Key Skills

Education

Bachelor's degree