Job Title

AI Engineer – Advanced RAG, Agents, and AI Ecosystem DevelopmentLocation: REMOTEDepartment: Data Science & AI InnovationEmployment Type: Full-timeRole Overview:We are seeking a highly skilled AI Engineer to join Data Science team and drive cutting-edge initiatives around Advanced Retrieval-Augmented Generation (RAG), Agent-Oriented Architectures (A2A Agents), Model Context Protocol (MCP)-based Ecosystems, and LLM-driven AI Solutions.This role will focus on designing, developing, deploying, and optimizing intelligent systems leveraging the latest in AI research, cloud-native practices, and microservice-based architecture.The ideal candidate will blend deep machine learning expertise, modern LLM application building, and cloud-native engineering skills to craft scalable, secure, and modular AI solutions aligned with enterprise objectives.Key Responsibilities:Architect and Implement Advanced RAG Pipelines: Design modular RAG systems integrating vector databases, memory systems, context-based retrieval, and multi-agent collaboration.Develop and Integrate Agent Ecosystems: Build multi-agent orchestration frameworks using MCP (Model Context Protocol) to enable secure, scalable A2A agent communications.LLM Application Engineering: Design and fine-tune solutions using state-of-the-art LLMs (GPT, Gemini, Claude, open-source models) focusing on customized tool use, function calling, context management, and efficient prompt engineering.Cloud-Native Deployment: Containerize applications using Docker and deploy them as scalable microserviceson GCP (Cloud Run, Kubernetes, Vertex AI) and other cloud environments.Exploratory Data Analysis & Feature Engineering: Perform deep data analysis, understand data quality, and design data pipelines for ML systems.Machine Learning Model Development and Optimization: Build predictive models (classification, regression, segmentation, NLP, CV) and continuously optimize them for performance and scalability.MLOps and Lifecycle Management: Ensure efficient deployment, versioning, monitoring, and retraining strategies for ML models.Cross-functional Collaboration: Work closely with Data Scientists, ML Engineers, and Business Analysts to ensure AI solutions are aligned with business goals and deliver measurable impact.Communicate Insights: Effectively present technical findings to both technical and non-technical stakeholders.Required Qualifications & Skills:Education:Graduate or Postgraduate Degree in Computer Science, Data Science, AI/ML, Mathematics, or a related field.Experience:Minimum 7+ years of relevant experience in Data Science/AI Engineering.Minimum 3+ years in roles focused on advanced AI architectures (RAG, agent systems, LLM applications).Proven experience with production-grade deployments of AI/ML systems on cloud environments.Technical Skills:Strong Programming: Python (Mandatory); PySpark, R (Desirable).Advanced AI/ML Expertise:Deep knowledge of ML algorithms (XGBoost, Random Forest, LSTM, BERT, OpenCV, etc.).Experience working with LLMs, embedding models, vector databases (FAISS, Milvus, Pinecone).Knowledge of multi-agent orchestration, RAG techniques, retrieval pipelines, and MCP protocol design is a big advantage.Cloud Expertise:Hands-on with GCP (Vertex AI, Cloud Run, Kubernetes); experience with AWS SageMaker is a plus.DevOps & MLOps:Containerization using Docker.Building CI/CD pipelines for model deployment and API serving.Visualization and Collaboration Tools:PowerBI (Basic), Alteryx (Basic), Jupyter Notebooks, Google Colab (Expert).Behavioral Competencies:Strategic thinker with the ability to translate complex business challenges into scalable AI solutions.Strong problem-solving mindset for ambiguous and evolving technical environments.Team player, capable of mentoring junior data scientists and engineers.Excellent communication skills, with the ability to explain complex concepts to diverse stakeholders.Preferred (Good to Have) Skills:Experience with LangChain, LangGraph, or similar agent orchestration frameworks.Familiarity with open-source fine-tuning frameworks (e.g., PEFT, LoRA, QLoRA).Knowledge of API design for multi-agent interaction (OpenAI function calling, tool usage frameworks).Exposure to knowledge graphs and multimodal RAG designs.Why Join Us?Be at the forefront of AI innovation (RAG, MCP, Agent ecosystems) at scale.Drive projects blending cutting-edge AI research with real-world applications.Work in a highly agile, forward-thinking environment with a top-tier data science team.Competitive compensation, learning opportunities, and career advancement in a growing enterprise.