Senior AI Engineer / Full Stack AI Engineer

Experience : 8+ Years (3+ Years in Generative AI/LLMs)

Location : Bengaluru / Chennai (Hybrid)

Employment Type : Full-Time

About the Role :

We are looking for a highly skilled Senior AI Engineer / Full Stack AI Engineer to design, develop, and deploy enterprise-grade AI applications powered by Large Language Models (LLMs), Agentic AI, and modern cloud-native technologies.

The ideal candidate will have hands-on experience building AI Agents, Multi-Agent Systems, Retrieval-Augmented Generation (RAG) applications, and intelligent automation platforms, while also possessing strong full-stack development expertise to build scalable, production-ready AI solutions. You will collaborate with Product Managers, Architects, Data Engineers, UX Designers, and business stakeholders to deliver innovative AI products that solve complex enterprise challenges across Retail, Commerce, Supply Chain, Customer Experience, and Digital Transformation.

Key Responsibilities :

1. AI Engineering :

- Design, develop, and deploy enterprise-grade AI applications using Large Language Models (LLMs) and Agentic AI frameworks.

- Build autonomous AI agents, multi-agent systems, and intelligent workflow orchestration solutions.

- Develop scalable Retrieval-Augmented Generation (RAG) pipelines using vector databases and enterprise knowledge sources.

- Implement prompt engineering, model orchestration, memory management, and AI evaluation frameworks to improve response quality and accuracy.

- Optimize AI models and workflows for performance, scalability, latency, and cost efficiency.

2. Full Stack Development :

- Develop secure backend services and REST APIs using Python, FastAPI, or Flask.

- Build responsive web applications and conversational AI interfaces using React, Next.js, JavaScript, or TypeScript.

- Integrate AI services with enterprise platforms including ERP, CRM, search platforms, and business applications.

- Design scalable microservices and event-driven architectures supporting AI workloads.

3. Cloud & Platform Engineering :

- Deploy AI applications on AWS, Azure, or Google Cloud Platform using cloud-native services.

- Containerize applications using Docker and Kubernetes.

- Build CI/CD pipelines and automate deployment processes using GitHub Actions or Azure DevOps.

- Implement monitoring, logging, tracing, and observability for production AI systems.

4. AI Governance & Security :

- Ensure AI applications adhere to responsible AI principles, security standards, and data privacy regulations.

- Implement guardrails, policy enforcement, model governance, and AI safety mechanisms.

- Develop monitoring frameworks to evaluate model performance, reliability, and compliance.

5. Technical Leadership :

- Participate in architecture discussions, technical design reviews, and solution planning.

- Define engineering standards, reusable components, and development best practices.

- Mentor junior engineers and contribute to the growth of the AI engineering practice.

- Stay updated with emerging advancements in Generative AI, Agentic AI, and enterprise AI technologies.

Required Skills & Experience :

The ideal candidate should possess strong expertise in designing, developing, and deploying enterprise-grade AI solutions powered by Large Language Models (LLMs), Agentic AI frameworks, and modern cloud-native technologies.

1. Artificial Intelligence & Generative AI :

- Hands-on experience working with Large Language Models (LLMs) such as OpenAI, Anthropic Claude, Gemini, Llama, and Mistral.

- Strong expertise in designing and implementing Agentic AI solutions, AI Agents, Multi-Agent Systems, and Retrieval-Augmented Generation (RAG) applications.

- Experience with Prompt Engineering, Semantic Search, AI Evaluation Frameworks, Model Benchmarking, AI Observability, and Fine-Tuning concepts to improve model performance and reliability.

2. AI Frameworks & Orchestration :

- Proven experience using enterprise AI frameworks including LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, DSPy, and Semantic Kernel for building scalable AI applications and autonomous workflows.

3. Backend Development :

- Strong programming skills in Python with experience developing scalable backend services using FastAPI or Flask.

- Experience building RESTful APIs, GraphQL services, Microservices Architecture, and Event-Driven Systems supporting enterprise AI applications.

4. Frontend Development :

- Experience developing modern, responsive web applications using React, Next.js, JavaScript, and TypeScript.

- Ability to build intuitive conversational user interfaces and AI-powered digital experiences.

5. Databases & Search Technologies :

- Experience working with relational and NoSQL databases including PostgreSQL, MySQL, MongoDB, and Redis.

- Hands-on experience implementing semantic search solutions using Elasticsearch/OpenSearch and vector databases such as Pinecone, Weaviate, Qdrant, Chroma, or Milvus.

6. Cloud & DevOps :

- Experience designing and deploying cloud-native applications on AWS, Microsoft Azure, or Google Cloud Platform (GCP).

- Strong understanding of containerization, orchestration, CI/CD pipelines, Infrastructure as Code, monitoring, and observability using technologies such as Docker, Kubernetes, GitHub Actions, Azure DevOps, and related cloud-native tools.

Preferred Domain Experience :

Candidates with experience delivering AI-powered enterprise solutions in the following domains will be preferred :

- Retail & Digital Commerce

- Omnichannel Commerce

- Merchandising & Product Management

- Product Discovery & Intelligent Search

- Customer Experience & Personalization

- Supply Chain & Inventory Optimization

- Retail Analytics

- Intelligent Process Automation

Educational Qualifications :

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related discipline.

- Minimum 8+ years of software engineering experience with at least 3+ years of hands-on experience in Generative AI, AI/ML, or enterprise AI solution development.

- Experience designing, developing, and deploying production-grade AI applications in enterprise environments.

- Professional certifications in AWS, Microsoft Azure, Google Cloud Platform, AI Engineering, or Cloud Architecture will be an added advantage.

- Exposure to Knowledge Graphs, Semantic Models, AI Cost Optimization (FinOps), Recommendation Systems, or Personalization Platforms is highly desirable.

Key Competencies :

- Enterprise AI Solution Architecture

- Generative AI & Agentic AI Development

- Large Language Models (LLMs)

- AI Agents & Multi-Agent Systems

- Retrieval-Augmented Generation (RAG)

- Full Stack Application Development

- Cloud-Native AI Engineering

- MLOps, AI Observability & Model Monitoring

- AI Governance, Security & Responsible AI

- Technical Leadership & Solution Design

- Stakeholder Management & Cross-Functional Collaboration

- Analytical Thinking, Problem Solving & Innovation.