Job Description

Senior AI / Machine Learning Engineer (LLMs, MLOps, Generative AI, Cloud Deployment) Location: Pune Experience: 7+ Years (AI, Machine Learning, Deep Learning & MLOps) About the Role We are seeking a Senior AI & Machine Learning Engineer with proven experience in building and deploying advanced AI systems at scale. The ideal candidate will have deep expertise in Large Language Models (LLMs) , Generative AI , MLOps , and cloud-based AI infrastructure . You?ll lead the end-to-end lifecycle of AI model development ? from research and architecture design to production deployment and optimization ? driving innovation in intelligent automation, RAG systems, and multi-agent AI frameworks. This is a strategic and hands-on role for someone passionate about shaping the future of enterprise AI solutions. Key Responsibilities AI / ML Development & Innovation Lead the design, development, and deployment of complex ML and deep learning models for NLP, computer vision, and multimodal AI applications. Work extensively with transformer architectures (GPT, LLaMA, Claude, T5, Gemini, etc.) for pre-training, fine-tuning, and inference optimization. Architect Retrieval-Augmented Generation (RAG) systems integrating vector databases (Pinecone, FAISS, Weaviate) with LLMs for contextual and domain-aware intelligence. Develop and scale LangChain-based applications and multi-agent AI systems enabling autonomous reasoning and orchestration. Drive prompt engineering and optimization strategies to enhance accuracy, efficiency, and token cost optimization. Implement model retraining pipelines, hyperparameter tuning , and continuous learning frameworks for performance improvement. Establish modular and reusable AI architectures enabling rapid experimentation and scalability across projects. MLOps & Production Deployment Architect and manage end-to-end MLOps pipelines using MLflow, Kubeflow, Airflow, or Vertex AI for continuous integration, deployment, and monitoring. Automate the full AI lifecycle ? from data ingestion and preprocessing to training, validation, CI/CD, and production rollout. Collaborate closely with Data Engineering and DevOps teams to ensure scalable, secure, and seamless cloud deployments (AWS, Azure, GCP). Build high-performance inference APIs using FastAPI, Flask, or TensorFlow Serving. Implement real-time monitoring , model drift detection , and automated rollback systems to maintain production reliability. Manage feature stores (Feast, Delta Lake) and data versioning systems (DVC, MLflow) to ensure reproducibility and governance. Cloud Architecture & Infrastructure Design and maintain cloud-native ML environments using AWS Sagemaker, Azure ML, or GCP Vertex AI. Use Docker, Kubernetes, and Terraform for containerized, scalable, and secure deployments. Optimize GPU/TPU utilization for distributed model training and large-scale inference workloads. Leverage serverless ML architectures (Lambda, Cloud Run) and multi-cloud strategies for resilience and cost efficiency. Integrate observability and performance monitoring using Prometheus, Grafana, and Evidently AI dashboards. Required Expertise 7+ years of hands-on experience in Machine Learning, Deep Learning, and AI system deployment . Strong understanding of LLMs, NLP, and Generative AI concepts and frameworks. Proficiency with TensorFlow, PyTorch, scikit-learn, LangChain, and Hugging Face Transformers . In-depth knowledge of MLOps tools ? MLflow, Airflow, Kubeflow, or Vertex AI. Experience deploying enterprise-scale AI applications across cloud environments (AWS / Azure / GCP) . Familiarity with vector databases, prompt engineering, and RAG systems . Excellent knowledge of data pipelines, CI/CD, and container orchestration . Preferred Qualifications Master?s or Ph.D. in Computer Science, Data Science, or Artificial Intelligence . Prior experience leading AI architecture, R&D initiatives , or multi-agent LLM systems . Strong track record of delivering production-grade AI solutions for SaaS, fintech, or automation-driven companies. Contributions to open-source projects or research publications in AI/ML fields. Soft Skills Strategic thinker with the ability to translate research into scalable business solutions. Excellent leadership, communication, and cross-functional collaboration skills. Passion for mentoring junior engineers and driving AI innovation culture.