Generative AI Engineer

Job Title: GenAI Engineer | AI/LLM Developer

Location: Bangalore

Experience: 46 Years

Employment Type: Full-Time, Permanent

Job Summary:

We are looking for a passionate and experienced GenAI Engineer / AI-LLM Developer to join our team in Bangalore and contribute to the development of next-generation AI-powered solutions in the Automotive domain.

The ideal candidate should have hands-on experience in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Prompt Engineering, and AI application development.

You will be responsible for building intelligent applications that automate the end-to-end defect management lifecycle by leveraging modern AI technologies, cloud platforms, and scalable backend services.

This role offers an opportunity to work on cutting-edge AI solutions, collaborate with cross-functional teams, and build enterprise-grade applications powered by Large Language Models.

Key Responsibilities:

1. Generative AI Development:

- Design, develop, and deploy enterprise-grade Generative AI applications.

- Build AI-powered solutions using Large Language Models (LLMs).

- Develop intelligent workflows for business process automation.

- Fine-tune prompts to optimize model responses and performance.

- Evaluate and improve AI model accuracy, reliability, and response quality.

- Integrate AI models into enterprise applications and workflows.

2. AI Use Cases:

Develop AI solutions for:

- Duplicate Ticket Detection

- AI-based Ticket Completeness Validation

- Intelligent Ticket Summarization

- Smart Ticket Routing

- AI-powered Ticket Creation Assistant

- Trace & Log Analysis

- Root Cause Analysis Assistance

- Knowledge Search using RAG

- Conversational AI Assistants

3. LLM & RAG Development:

- Develop applications using OpenAI APIs or equivalent LLM platforms.

- Build Retrieval-Augmented Generation (RAG) pipelines.

- Design semantic search solutions using Vector Databases.

- Implement embedding generation and similarity search.

- Optimize prompt templates and retrieval strategies.

- Evaluate LLM responses using appropriate evaluation metrics.

4. Backend Development:

- Design and develop RESTful APIs using Python (FastAPI preferred).

- Build scalable backend services for AI applications.

- Integrate AI services with enterprise systems.

- Optimize API performance and reliability.

- Implement authentication, authorization, and security best practices.

5. Data Engineering & Integration:

- Work with Databricks for AI workflows and data processing.

- Process structured and unstructured datasets.

- Build data pipelines supporting AI model training and inference.

- Integrate Vector Databases with enterprise applications.

- Collaborate with Data Engineers to optimize data availability.

6. Frontend Integration:

- Collaborate with frontend developers using JavaScript.

- Integrate AI services with user-facing applications.

- Support development of interactive AI-driven user interfaces.

7. DevOps & Deployment:

- Manage source code using Git.

- Participate in CI/CD implementation and deployment.

- Monitor AI applications in production.

- Troubleshoot production issues and optimize application performance.

8. Collaboration:

- Work closely with Product Managers, Software Engineers, QA teams, Data Scientists, and Business Stakeholders.

- Participate in Sprint Planning, Daily Stand-ups, Design Reviews, and Retrospectives.

- Prepare technical documentation and architectural recommendations.

- Mentor junior engineers and share AI best practices.

Required Technical Skills:

1. Generative AI:

- Generative AI (GenAI), Large Language Models (LLMs), Prompt Engineering, Prompt Optimization, AI Agents, AI Workflow Automation.

2. LLM Platforms:

- OpenAI API, Azure OpenAI, Anthropic Claude, Google Gemini, Hugging Face Transformers, LangChain, LlamaIndex.

3. Retrieval-Augmented Generation (RAG):

- RAG Architecture, Semantic Search, Embedding Models, Knowledge Retrieval, Context Management.

4. Vector Databases:

- Pinecone, ChromaDB, FAISS, Weaviate, Milvus, Vector Search.

5. Programming Languages:

- Python, JavaScript.

6. Backend Development:

- FastAPI, Flask (Preferred), REST APIs, API Integration.

7. Data Engineering:

- Databricks, SQL, Data Processing, Data Pipelines.

8. DevOps & Tools:

- Git, CI/CD, Docker (Preferred), Kubernetes (Good to Have).

9. Project Management:

- Jira, Agile, Scrum, SDLC.

Preferred Skills:

- Experience in the Automotive domain.

- Hands-on experience with Trace & Log Analysis.

- Exposure to AI Agents and Agentic AI frameworks.

- Experience building enterprise AI copilots.

- Knowledge of Microservices architecture.

- Cloud experience with Azure, AWS, or GCP.

- Familiarity with MLOps concepts and deployment pipelines.

- German language proficiency is an added advantage.

- Strong debugging and analytical skills.

Candidate Profile:

- 46 years of experience in AI Engineering, Machine Learning, or Software Development.

- Hands-on experience building applications using Generative AI and LLMs.

- Strong expertise in Prompt Engineering and Retrieval-Augmented Generation (RAG).

- Experience integrating OpenAI APIs or equivalent LLM platforms.

- Good understanding of Vector Databases and semantic search.

- Strong programming skills in Python and REST API development.

- Experience working with Databricks and modern data platforms.

- Familiarity with JavaScript-based frontend integration.

- Excellent analytical, communication, and problem-solving skills.

- Ability to work effectively in Agile development Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Information Technology, Data Science, or a related field.

Good-to-Have Skills:

- LangGraph, CrewAI, AutoGen, Azure AI Studio, Azure OpenAI, AWS Bedrock, Vertex AI, Neo4j Knowledge Graphs, MLflow, Docker, Kubernetes, Redis, Elasticsearch.

Soft Skills:

- Strong analytical and critical thinking skills.

- Excellent verbal and written communication.

- Ability to work collaboratively across cross-functional teams.

- Strong ownership and problem-solving mindset.

- Passion for AI innovation and continuous learning.

(ref:hirist.tech)