Artificial Intelligence Engineer

Role & responsibilities Technical Skills Advanced proficiency in Python.Extensive experience with LLM frameworks (Hugging Face Transformers, LangChain) and prompt engineering techniquesExperience with big data processing using Spark for large-scale data analyticsVersion control and experiment tracking using Git and MLflowSoftware Engineering & Development: Advanced proficiency in Python, familiarity with Go or Rust, expertise in microservices, test-driven development, and concurrency processing.DevOps & Infrastructure: Experience with Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations.LLM Infrastructure & Deployment: Proficiency in LLM serving platforms such as vLLM and FastAPI, model quantization techniques, and vector database management.MLOps & Deployment: Utilization of containerization strategies for ML workloads, experience with model serving tools like TorchServe or TF Serving, and automated model retraining.Cloud & Infrastructure: Strong grasp of advanced cloud services (AWS, GCP, Azure) and network security for ML systems.LLM Project Experience: Expertise in developing chatbots, recommendation systems, translation services, and optimizing LLMs for performance and security.General Skills: Python, SQL, knowledge of machine learning frameworks (Hugging Face, TensorFlow, PyTorch), and experience with cloud platforms like AWS or GCP.Experience in creating LLD for the provided architecture.Experience working in microservices based architecture. Domain Expertise Deep understanding of ML and LLM development lifecycle, including fine-tuning and evaluationExpertise in feature engineering, embedding optimization, and dimensionality reductionAdvanced knowledge of A/B testing, experimental design, and statistical hypothesis testingExperience with RAG systems, vector databases, and semantic search implementationProficiency in LLM optimization techniques including quantization and knowledge distillationUnderstanding of MLOps practices for model deployment and monitoring Professional Competencies Strong analytical thinking with ability to solve complex ML challengesExcellent communication skills for presenting technical findings to diverse audiencesExperience translating business requirements into data science solutionsProject management skills for coordinating ML experiments and deploymentsStrong collaboration abilities for working with cross-functional teamsDedication to staying current with latest ML research and best practicesAbility to mentor and share knowledge with team members AI Engineer (Agentic App Experience / LLM Integration) Summary: Engineer focused on delivering application features powered by agentic workflows, integrating with MLOps-managed agents, consuming LLM capabilities via an AI Gateway, and shipping reliable production experiences. Responsibilities Build app-facing AI features that consume agentic workflows exposed by MLOps platforms Integrate LLM capabilities via AI Gateway (routing, auth, policy controls, logging) Implement orchestration patterns: tool/function calling, context handling, prompt/version management Ensure production readiness: latency optimization, evals, guardrails, monitoring, and incident support Partner with security/compliance and platform teams to meet governance and data controls Must Have Experience building production AI integrations (LLMs, agents/orchestration, APIs) Strong engineering fundamentals: testing, observability, reliability, secure integration Familiarity with prompt/versioning, evaluation methods, and safety/quality controls Preferred candidate profile GenAI / LLM Engineers (Python, FastAPI, Microservices, K8s, RAG, VectorDBs) LLMs, agents/orchestration, APIs, AI integrations, Prompt/versioning .