Artificial Intelligence Engineer

PwC AC is hiring for AI Engineer Apply and get a chance to work with one of the Big4 companies #PwC AC. Job Titl e: AI Engineer Years of Experience : 3-7 years Shift Timing s: 11AM-8PM Qualificatio n: Graduate and above(Full time) About PwC CTIO ? EmTech PwC?s Commercial Technology and Innovation Office (CTIO) is committed to shaping the future of technology within the firm. The EmTech (Emerging Technologies) division pioneers next-gen AI/ML/GenAI solutions, explores groundbreaking technologies, and delivers scalable innovation. We blend engineering rigor with applied AI research to transform ideas into real-world impact. We are at the intersection of software engineering and AI innovation ? pushing boundaries in LLMOps , MLOps , and AI infrastructure to enable agile, secure, and scalable delivery of AI-powered products across domains. Role Overview We are seeking a Senior Associate ? AI Engineer / MLOps / LLMOps with a passion for building resilient, cloud-native AI systems. In this role, you?ll collaborate with data scientists, researchers, and product teams to build infrastructure, automate pipelines, and deploy models that power intelligent applications at scale. If you enjoy solving real-world engineering challenges at the convergence of AI and software systems, this role is for you. Key Responsibilities Architect and implement AI/ML/GenAI pipelines , automating end-to-end workflows from data ingestion to model deployment and monitoring. Develop scalable, production-grade APIs and services using FastAPI, Flask , or similar frameworks for AI/LLM model inference. Design and maintain containerized AI applications using Docker and Kubernetes . Operationalize Large Language Models (LLMs) and other GenAI models via cloud-native deployment (e.g., Azure ML, AWS Sagemaker, GCP Vertex AI). Manage and monitor model performance post-deployment, applying concepts of MLOps and LLMOps including model versioning, A/B testing, and drift detection. Build and maintain CI/CD pipelines for rapid and secure deployment of AI solutions using tools such as GitHub Actions, Azure DevOps, GitLab CI . Implement security, governance, and compliance standards in AI pipelines. Optimize model serving infrastructure for speed, scalability, and cost-efficiency. Collaborate with AI researchers to translate prototypes into robust production-ready solutions. Required Skills & Experience 3 to 7 years of hands-on experience in AI/ML engineering, MLOps, or DevOps for data science products. Bachelor's degree in Computer Science, Engineering, or related technical field (BE/BTech/MCA). Strong software engineering foundation with hands-on experience in Python , Shell scripting , and familiarity with ML libraries (scikit-learn, transformers, etc.). Experience deploying and maintaining LLM-based applications , including prompt orchestration, fine-tuned models , and agentic workflows . Deep understanding of containerization and orchestration (Docker, Kubernetes, Helm). Experience with CI/CD pipelines , infrastructure-as-code tools (Terraform, CloudFormation), and automated deployment practices. Proficiency in cloud platforms : Azure (preferred), AWS, or GCP ? including AI/ML services (e.g., Azure ML, AWS Sagemaker, GCP Vertex AI). Experience managing and monitoring ML lifecycle (training, validation, deployment, feedback loops). Solid understanding of APIs, microservices, and event-driven architecture . Experience with model monitoring/orchestration tools (e.g, Kubeflow, MLflow). Exposure to LLMOps-specific orchestration tools such as LangChain, LangGraph, Haystack, or PromptLayer. Experience with serverless deployments (AWS Lambda, Azure Functions) and GPU-enabled compute instances. Knowledge of data pipelines using tools like Apache Airflow, Prefect, or Azure Data Factory. Exposure to logging and observability tools like ELK stack, Azure Monitor, or Datadog. Good to Have Experience implementing multi-model architecture , serving GenAI models alongside traditional ML models. Knowledge of data versioning tools like DVC, Delta Lake, or LakeFS. Familiarity with distributed systems and optimizing inference pipelines for throughput and latency. Experience with infrastructure cost monitoring and optimization strategies for large-scale AI workloads. It would be great if the candidate has exposure to full-stack ML/DL. Soft Skills & Team Expectations Strong communication and documentation skills; ability to clearly articulate technical concepts to both technical and non-technical audiences. Demonstrated ability to work independently as well as collaboratively in a fast-paced environment. A builder's mindset with a strong desire to innovate, automate, and scale . Comfortable in an agile, iterative development environment. Willingness to mentor junior engineers and contribute to team knowledge growth. Proactive in identifying tech stack improvements, security enhancements, and performance bottlenecks. Preferred Certifications (at least two are preferred) Cloud Certifications (Azure, AWS, GCP) ? e.g., Azure DevOps Engineer , AWS Certified ML Specialist , GCP Professional ML Engineer Docker/Kubernetes/Cloud Native certifications (CKAD, CKA, etc.) MLOps Certifications (e.g., Coursera MLOps Specialization, Google MLOps certification) Python or software engineering certifications (e.g., PCAP, PCEP) Why Join PwC CTIO? Work at the cutting edge of AI engineering , deploying real solutions at production scale. Collaborate with visionary AI researchers , engineers, and product leaders to redefine what?s possible. Be part of a team that values both rapid experimentation and engineering excellence . Gain exposure to LLMOps and GenAI infrastructure at an enterprise scale. Join a culture that encourages continuous learning, bold thinking , and inclusive innovation .