AI Engineer - Agentic AI & Generative AI

Location : Chennai / Bengaluru

Experience : 2- 7 Years

Employment Type : Full-Time

Department : Data Science & Artificial Intelligence

About the Role :

We are seeking an innovative AI Engineer with expertise in Generative AI (GenAI) and Agentic AI to design, develop, and deploy next-generation AI-powered applications and autonomous intelligent systems. The ideal candidate will have hands-on experience building enterprise-grade AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, LangChain, and Multi-Agent Architectures. In this role, you will collaborate with cross-functional teams to develop scalable AI platforms, intelligent automation workflows, and production-ready AI applications that solve complex business problems across multiple domains.

Key Responsibilities :

1. AI Solution Architecture :

- Design scalable, secure, and cloud-native AI solution architectures aligned with business goals and enterprise technology standards.

- Evaluate business requirements and recommend appropriate AI models, frameworks, and deployment strategies.

- Define reusable AI architecture patterns and best practices for enterprise implementations.

- Collaborate with solution architects, product teams, and business stakeholders during solution design.

2. Agentic AI Development :

- Design and develop autonomous AI agents capable of reasoning, planning, decision-making, and executing complex multi-step tasks.

- Build multi-agent systems where specialized AI agents collaborate to accomplish business workflows.

- Develop intelligent orchestration frameworks for agent communication, task routing, memory management, and tool integration.

- Implement autonomous workflow automation using Agentic AI principles.

3. Generative AI & Large Language Models :

- Develop AI-powered applications using Large Language Models (LLMs).

- Integrate OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini, Llama, Mistral, or similar foundation models.

- Design prompt engineering strategies for high-quality, context-aware AI responses.

- Fine-tune, evaluate, and optimize foundation models for domain-specific use cases.

- Develop conversational AI assistants, enterprise copilots, document intelligence, and knowledge assistants.

4. Retrieval-Augmented Generation (RAG) :

- Design and implement enterprise RAG pipelines.

- Build ingestion frameworks for structured and unstructured enterprise documents.

- Develop semantic search solutions using vector databases.

- Implement document chunking, embedding generation, indexing, retrieval optimization, and response synthesis.

- Optimize retrieval quality and minimize hallucinations through advanced RAG techniques.

5. AI Application Development :

- Develop backend APIs and AI microservices supporting enterprise AI applications.

- Build interactive web-based AI applications and user interfaces.

- Integrate AI models with enterprise applications, APIs, databases, and third-party systems.

- Develop scalable AI services capable of handling high transaction volumes.

6. MLOps & AI Operations :

- Build CI/CD pipelines for AI model deployment.

- Automate model training, testing, deployment, and monitoring.

- Implement model versioning, experiment tracking, and reproducibility.

- Monitor model performance, latency, drift, and reliability in production.

- Support continuous model improvement through feedback loops.

7. Cloud & Platform Engineering :

- Deploy AI workloads on Microsoft Azure, AWS, or Google Cloud Platform.

- Develop containerized AI applications using Docker and Kubernetes.

- Build scalable AI infrastructure supporting enterprise production environments.

- Optimize infrastructure for performance, security, and cost.

8. AI Governance, Security & Responsible AI :

- Implement responsible AI practices including explainability, fairness, transparency, and bias detection.

- Ensure compliance with enterprise AI governance, security, privacy, and regulatory requirements.

- Protect sensitive enterprise data through secure AI architecture and access controls.

- Develop monitoring frameworks for AI safety and compliance.

9. Technical Leadership :

- Participate in solution architecture reviews and technical design discussions.

- Establish coding standards and AI engineering best practices.

- Conduct code reviews and mentor junior engineers.

- Evaluate emerging AI technologies and recommend adoption strategies.

- Drive innovation initiatives related to Agentic AI and enterprise Generative AI.

Required Skills & Experience :

The ideal candidate should have hands-on experience in designing and deploying enterprise AI solutions using modern Generative AI and Agentic AI technologies.

1. Artificial Intelligence & Machine Learning :

- Strong understanding of Generative AI, Large Language Models (LLMs), NLP, Machine Learning, Deep Learning, and AI application development.

- Experience building enterprise-grade AI assistants, copilots, and intelligent automation solutions.

2. Agentic AI :

- Hands-on experience developing AI Agents, autonomous workflows, multi-agent systems, and intelligent orchestration frameworks.

- Knowledge of agent planning, memory management, reasoning, and tool execution.

3. Generative AI Frameworks :

- Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, DSPy, or similar AI orchestration frameworks.

- Strong prompt engineering and LLM optimization skills.

- Experience integrating OpenAI, Azure OpenAI, Claude, Gemini, Llama, or other foundation models.

4. RAG & Vector Search :

- Experience building Retrieval-Augmented Generation (RAG) applications.

- Hands-on knowledge of embedding models, semantic search, vector databases, and document retrieval pipelines.

- Experience with Pinecone, Weaviate, Milvus, ChromaDB, FAISS, or Azure AI Search.

5. Programming :

- Strong proficiency in Python.

- Experience developing REST APIs using FastAPI or Flask.

- Knowledge of JavaScript or TypeScript is an added advantage.

6. Cloud Technologies :

- Experience deploying AI applications on Microsoft Azure, AWS, or Google Cloud Platform.

- Familiarity with Azure OpenAI, AWS Bedrock, Vertex AI, or equivalent AI services.

7. MLOps & DevOps :

- Experience with Git, Docker, Kubernetes, CI/CD pipelines, MLflow, LangSmith, and model monitoring tools.

- Knowledge of model deployment, observability, and production AI operations.

8. Databases :

- SQL and NoSQL databases.

- Vector databases.

- Enterprise data integration.

Preferred Qualifications :

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

- Relevant certifications in Azure AI Engineer, AWS Machine Learning, Google Professional Machine Learning Engineer, or Generative AI technologies will be an added advantage.