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
Full Stack Development
Application Support
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).
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