About the Role
We are looking for a highly skilled and innovative AI / ML Engineer (Contract role) to join our Agentic AI and Generative AI (GenAI) development team. The ideal candidate will be hands-on in building and optimizing Multimodal RAG architectures, intelligent agents, and enterprise-grade GenAI automation systems.
This role offers a unique opportunity to work on next-generation Agentic AI frameworks, combining LLMs, multimodal retrieval, and automation pipelines for global enterprise use cases.
Key Responsibilities
- Design, build, and deploy Multimodal RAG architectures and LangGraph-based Agentic AI systems for enterprise automation.
- Develop and optimize chunking strategies, embedding pipelines, and vector database integrations (Vertex AI, PGVector, Pinecone).
- Integrate LLMs (Gemini 2.5, Gemma, LLaMA 4, etc.) into GenAI workflows and automation pipelines.
- Implement and maintain CI / CD pipelines, supporting MLOps / LLMOps frameworks for continuous delivery.
- Collaborate with cross-functional teams to ensure scalable deployment on GCP (Vertex AI) platforms.
- Continuously evaluate, monitor, and fine-tune models for precision, recall, relevance, faithfulness, and cost efficiency.
- Contribute to the development of evaluation frameworks (e.g., RAGAS) and AI efficacy dashboards.
Required Skills & Experience
Minimum 4+ years of experience in AI / ML model development with a focus on GenAI, RAG, or Agentic AI systems.Proven hands-on experience with :Vector databases : PGVector, Pinecone, or similarMultimodal data processing : text, image, audio embeddings and chunkingEmbedding models and vector search optimizationPractical knowledge of LangChain, LangGraph, and LLMOps toolkits.Familiarity with GCP Vertex AI or equivalent ML platforms.Experience with CI / CD, MLflow, or other MLOps automation pipelines.Exposure to prompt engineering, fine-tuning, and LLM model integration.Good to Have
Bachelor’s or Master’s degree in Computer Science, AI / ML, Data Science, or related disciplines.GCP / AWS / ML certifications are an advantage.Contributions to open-source AI projects or publications in GenAI / RAG / Agentic AI domains.Strong problem-solving ability and a mindset for rapid prototyping and experimentation.