Role Overview

We are seeking a Senior AI/ML Engineer with a proven track record of designing, building, and deploying end-to-end Machine Learning, Deep Learning, and Generative AI solutions. In this role, you will bridge cutting-edge AI research and production-grade engineering—applying advanced statistical rigor, calculus, NLP, Computer Vision, and modern GenAI architectures to solve complex organizational problems. You will also lead MLOps practices and enforce cloud security standards to ensure scalable, high-throughput model deployment that generates clear business value.

Key Responsibilities

Production AI/ML Engineering: Design, build, test, and deploy scalable Machine Learning, Deep Learning, and Generative AI pipelines that directly deliver demonstrable business value.
Generative AI Systems: Architect and implement advanced GenAI systems utilizing Retrieval-Augmented Generation (RAG), Graph RAG, Agentic Workflows, and model tuning techniques.
Domain Applications: Implement state-of-the-art Natural Language Processing (NLP) and/or Computer Vision (CV) architectures to solve complex unstructured data challenges.
Statistical & Mathematical Modeling: Apply deep theoretical and practical knowledge of calculus, probability, and econometrics to formulate, validate, and optimize model performance.
MLOps & Infrastructure: Spearhead CI/CD pipelines for ML, automated training/inference workflows, model monitoring, and cloud security compliance across enterprise environments.
Technical Leadership: Work closely with product leads, software engineers, and business stakeholders to translate strategic goals into robust technical deliverables.

Required Qualifications & Experience

Experience: 5+ years of hands-on experience in AIML engineering, with a track record of deploying production-grade Machine Learning solutions.
Foundational Mathematics & Statistics: Strong theoretical and applied grounding in calculus, linear algebra, and econometric/statistical methods:
Linear & Logistic Regression, Generalized Linear Models (GLM)
Time Series Analysis, Survival Analysis, Sampling Techniques
Dimension Reduction & Clustering: PCA, Factor Analysis, Multidimensional Scaling, Clustering
Decision Trees: CART, CHAID, Discriminant Analysis
Classical Machine Learning: Expert implementation of algorithms including Random Forest, Support Vector Machines (SVM), Gradient Boosting Machines (GBM), XGBoost, and ensemble approaches.
Deep Learning Frameworks: Direct experience with CNNs, RNNs, and modern Transformer Architectures as applied to NLP and/or Computer Vision.
Generative AI Stack: Direct engineering experience with:
RAG (Retrieval-Augmented Generation) & Graph RAG
Agentic Workflows & Multi-agent frameworks
Model Fine-Tuning (PEFT, LoRA, instruction tuning)
MLOps & Cloud Security: Extensive experience in automated ML deployment pipelines, continuous monitoring, containerization, and cloud security best practices (AWS, GCP, or Azure).

Technical Stack & Preferred Skills

Languages & Frameworks: Python, PyTorch, TensorFlow, SQL, C++/CUDA (a plus).
AI Tooling & Databases: Vector databases (e.g., Pinecone, Milvus, Qdrant), graph databases (Neo4j), and orchestration frameworks (LangChain, LlamaIndex).
Infrastructure: Docker, Kubernetes, Terraform, MLflow, Kubeflow, or cloud-native MLOps tools.
Soft Skills: Problem-solving mindset with a focus on business impact and clear technical communication.