Note: The job is a remote job and is open to candidates in USA. Diverse Lynx is a company seeking a Senior AI Engineer to lead development initiatives using AI technologies. The role involves designing and building full stack web applications, developing APIs, and integrating AI features while collaborating with cross-functional teams.

Responsibilities

• Lead spec-first development initiatives using GitHub Spec Kit — authoring specs, technical plans, and agent-ready task breakdowns before writing any code
• Design and build full stack web applications using React, JavaScript/TypeScript frameworks, and Node.js, from UI to backend API layer
• Develop, integrate, and maintain RESTful and GraphQL APIs, ensuring performance, reliability, and security across services
• Architect and deploy cloud-native solutions on AWS (Lambda, EC2, S3, API Gateway, RDS, CloudFormation) with a focus on scalability and cost efficiency
• Build and integrate AI-powered features — leveraging LLMs, AI agents, prompt engineering, and the GenAI ecosystem to enhance product capabilities
• Design and manage relational (PostgreSQL) and document (MongoDB) databases, including schema design, query optimisation, and data migrations
• Collaborate with product managers, designers, and AI/ML engineers to translate requirements into well-specified, shippable software
• Participate in code reviews, establish engineering best practices, and contribute to a culture of quality and continuous improvement

Skills

• 5+ years of professional experience in full stack software development
• Proven hands-on experience with GenAI tools and a spec-first development approach, including GitHub Spec Kit or equivalent workflows
• Strong proficiency in React and modern JavaScript / TypeScript frameworks (Next.js, Vue, or similar)
• Solid backend development skills with Node.js — building and maintaining production REST or GraphQL APIs
• Experience deploying and operating applications on AWS — comfortable with core services such as Lambda, EC2, S3, API Gateway, and RDS
• Practical experience with both MongoDB (document store) and PostgreSQL (relational), including schema design and query tuning
• Familiarity with AI agent frameworks, LLM APIs (OpenAI, Anthropic, or similar), and prompt engineering techniques
• Strong understanding of software engineering fundamentals — data structures, system design, testing, and CI/CD practices
• Bachelor's degree in computer science, Engineering, or equivalent practical experience
• Supervised Learning: Linear regression and logistic regression, Decision trees, Random Forest, Gradient Boosting (XGBoost, LightGBM, CatBoost), Support Vector Machines (SVMs) and kernel methods, Neural networks — CNNs, RNNs, LSTMs, and Transformers, Classification, regression, and ranking problems, Cross-validation, bias-variance trade-off, regularization (L1/L2, dropout)
• Unsupervised Learning: Clustering: K-Means, DBSCAN, Gaussian Mixture Models, hierarchical clustering, Dimensionality reduction: PCA, t-SNE, UMAP, Autoencoders and variational autoencoders (VAEs), Anomaly detection and outlier identification, Association rule mining (Apriori, FP-Growth), Topic modelling (LDA, NMF)
• Reinforcement Learning: Markov Decision Processes (MDPs) states, actions, rewards, transitions, Model-free methods: Q-Learning, SARSA, Deep Q-Networks (DQN), Policy gradient methods: REINFORCE, PPO, A3C / A2C, Actor-Critic architectures, Multi-armed bandits and contextual bandits, Reward shaping, environment design, and simulation frameworks (OpenAI Gym)
• Relevant learning algorithms - Adjacent & advanced techniques: Transfer learning and fine-tuning pre-trained models, Semi-supervised and self-supervised learning, Active learning and human-in-the-loop pipelines, Federated learning for privacy-preserving training, Bayesian optimization and hyperparameter tuning (Optuna, Ray Tune), Ensemble methods, stacking, and model blending, Graph Neural Networks (GNNs) a plus, Causal inference and counterfactual reasoning — a plus
• Experience with GitHub Copilot, Cursor, or other AI-assisted coding environments in day-to-day development
• Familiarity with containerization (Docker, Kubernetes) and infrastructure-as-code (Terraform, AWS CDK)
• Exposure to vector databases (Pinecone, pgvector) or RAG (Retrieval-Augmented Generation) pipelines
• Knowledge of event-driven architectures using AWS SQS, SNS, or Event Bridge
• Experience with LangChain, LlamaIndex, or similar AI orchestration frameworks
• Contributions to open-source projects or a portfolio of AI-integrated applications

Company Overview

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