Note: The job is a remote job and is open to candidates in USA. Trident Consulting is a premier IT staffing firm providing high-impact workforce solutions to Fortune 500 and mid-market clients. They are seeking a Senior AI Engineer to lead spec-first development initiatives and design full stack web applications while integrating AI-powered features.
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
- Familiarity with observability tools — Data Dog, CloudWatch, or Splunk — for monitoring AI and API workloads
Benefits
- Contract (W2)
- Remote
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