AI Engineer

Responsibilities & Key Deliverables

We are looking for an experienced Senior Generative AI Engineer to join our GenAI Solutions team. The ideal candidate will bring extensive expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI systems, along with a strong background in cloud-native development and enterprise-scale deployments. This role is critical in driving our AI innovation strategy and delivering impactful, production-ready solutions for business transformation.

• Lead the end-to-end design, development, and deployment of Generative AI applications, including LLM-powered workflows, RAG pipelines, and Agentic AI solutions.
• Architect secure and scalable AI infrastructures using cloud platforms (GCP, AWS, Azure).
• Apply fine-tuning, advanced prompt engineering, and LLMOps best practices to enhance solution performance and relevance.
• Build, optimize, and maintain APIs and microservices (FastAPI, REST, Spring Boot) to integrate AI solutions into enterprise applications.
• Develop and manage data pipelines, vector databases, and Elasticsearch integrations for real-time knowledge retrieval and decision-making.
• Implement MLOps, CI/CD, and DevOps practices using Kubernetes, Docker, Jenkins, and Ansible for robust deployment and monitoring.
• Ensure high availability, logging, and performance monitoring through ELK stack and infrastructure-as-code practices.
• Collaborate with cross-functional teams to align AI solutions with business goals and operational requirements.
• Mentor and guide junior engineers, setting best practices for scalable AI development.

Experience

• 2+ years of professional software engineering experience, with Generative AI/LLM application development.

Qualifications

• A master’s degree in computer science/applications/engineering.
• Proven expertise in LLMs, LangChain, LangGraph, RAG systems, and Agentic AI frameworks.
• Proficiency in Python (advanced).
• Strong knowledge of cloud platforms: GCP, AWS, Azure.
• Hands-on experience with MLOps & DevOps practices: Kubernetes, Docker, Jenkins, Ansible, Infrastructure-as-Code.
• Experience with databases and data pipelines: PostgreSQL, SQL, Vector Databases, Elasticsearch.
• Familiarity with API development frameworks: FastAPI.
• Excellent analytical and problem-solving skills with the ability to lead AI initiatives end-to-end.
• Strong communication skills with experience in working across cross-functional and distributed teams.

General Requirements

• Domain experience in automotive, manufacturing, or enterprise AI solutions.
• Background in enterprise-grade AI security and compliance.
• Exposure to frontend integration frameworks (ReactJS) for AI-enabled applications.