We are seeking a highly skilled MLOps Engineer to bridge the gap between machine learning development and production-grade software engineering. In this role, you will be responsible for building the infrastructure and pipelines that allow our Agentic AI systems to scale, ensuring that our models are not just smart in a notebook, but robust, integrated, and high-performing in the real world. Key Responsibilities API Development & Integration: Design, develop, and maintain high-performance REST APIs using FastAPI or Django to serve machine learning models and integrate them with enterprise applications. Agentic AI Orchestration: Build and optimize frameworks for Agentic AI , focusing on multi-agent collaboration, tool-use integration, and autonomous decision-making loops. Data Architecture: Manage and optimize data flows between our AI services and relational databases, specifically SQL Server and PostgreSQL . Pipeline Automation (CI/CD): Implement automated pipelines for model training, testing, and deployment (CI/CD/CT) to ensure seamless updates and version control. Scalability & Monitoring: Monitor model performance in production (detecting drift and latency issues) and ensure the infrastructure scales to meet demand. Technical Requirements Core Languages- Expert-level Python proficiency. Web Frameworks- Hands-on experience with FastAPI (preferred for high-performance) or Django . Database Management- Strong SQL skills with PostgreSQL and Microsoft SQL Server . AI Specialization- Experience with Agentic AI frameworks (e.g., LangChain, AutoGen, or CrewAI). API Design- Expert knowledge of RESTful API architecture and integration patterns. Cloud & DevOps- Familiarity with Docker, Kubernetes, and cloud providers (AWS/Azure/GCP). Preferred Qualifications Experience with Vector Databases (e.g., Pinecone, Milvus, or Weaviate) for RAG-based systems. Knowledge of model quantization and optimization for production deployment. Experience implementing security best practices for AI APIs (OAuth2, JWT). Familiarity with ML monitoring tools like Prometheus, Grafana, or Weights & Biases. Soft Skills & Competencies Problem-Solving Mindset: Ability to decompose complex AI challenges into manageable engineering tasks and troubleshoot non-deterministic model behaviors. Collaborative Communication: Capable of translating technical ML concepts for stakeholders and working closely with Data Scientists to transition models from research to production. Adaptability: A proactive learner who stays current with the rapid evolution of Agentic AI and MLOps tooling. Ownership & Reliability: Strong sense of accountability for the stability of production systems and the integrity of data pipelines. Analytical Thinking: Data-driven approach to decision-making, especially when evaluating model performance metrics and infrastructure costs Note : We are looking for a builder who thrives at the intersection of backend engineering and cutting-edge AI. If you enjoy turning complex research into reliable, scalable products, we want to hear from you.