AI Full stack Engineer

AI Engineer with strong Full Stack Development experience to design, build, and deploy production-ready AI infrastructure and machine learning models. This role goes beyond traditional AI engineering — the ideal candidate will also drive application support, business stakeholder engagement, and release planning/management

 

Resource  will architect Agentic AI workflows, RAG (Retrieval-Augmented Generation) systems, and high-performance data pipelines to enable digital transformation through Generative AI, Computer Vision, and NLP solutions — while also owning the end-to-end lifecycle of current systems in production.

AI/ML Engineering

•Design, develop, and deploy production-ready AI infrastructure and machine learning models•Architect Agentic AI workflows and RAG systems for enterprise use cases•Build high-performance, scalable data pipelines to support AI/ML model training and inference•Develop and implement solutions leveraging Generative AI, Computer Vision, and NLP•Optimize model performance, latency, and cost for production environments•Stay current with emerging AI/ML frameworks, tools, and best practices

Full Stack Development

•Design and develop front-end and back-end components to support production applications•Build robust APIs and integrate AI/ML models into web and enterprise applications•Ensure application scalability, security, and performance across the stack

Application Support

•Provide ongoing production support for Production applications, including troubleshooting, root cause analysis, and issue resolution•Monitor application health, performance, and reliability post-deployment•Create and maintain support documentation, runbooks, and knowledge base articles•Respond to incidents and manage escalations in a timely manner

Experience: Bachelor’s degree with 5–8 years in AI architecture or ML development.

 

Core AI/ML: Proficiency in Python, AI/ML algorithms, NLP, Computer Vision, and cloud AI platforms (e.g., Vertex-AI).

 

Generative AI: Expertise in Agentic AI, RAG, MCP tools, and frameworks like LangChain or LlamaIndex.

 

LLM & Fine-tuning: Hands-on experience with LLMs (GPT, Claude, Llama) and fine-tuning models for custom production datasets.

 

Infrastructure: Experience with Docker, Kubernetes, and building robust API frameworks.

 

Data Systems: Proficiency in SQL, NoSQL, Graph, and Vector databases (e.g., BigQuery, Databricks).

 

Process & Soft Skills: Strong Agile knowledge, requirement gathering, and the ability to manage global stakeholders through clear communication and problem-solving. Strong understanding of application support processes, incident management, and monitoring tools

Excellent stakeholder management and business engagement skills

 

Good to Have:

 

Any AI related certifications.

Experience in the Automotive industry and related compliance domains.

Specific exposure to GCP data services (Cloud SQL, Postgres).