Req#57854-1 / 57855-1 / 57856-1 / 57857-1 / 57858-1 are same.Please do not submit dupliate profiles.
Role:FSE Sr.AI Engineer
Atlanta, GA
Hybrid or Remote is acceptable.
Rate:$80 to $85/hr
Top 3 skills required for this role:
1. Hands-on experience with GitHub Spec Kit and spec-driven development using AI agents (/specify, /plan, /tasks workflow).
2. Production-grade applications built with React / JavaScript frameworks and Node.js REST/GraphQL APIs.
3. AWS infrastructure (Lambda, S3, EC2, API Gateway) paired with MongoDB and/or PostgreSQL at scale.
Job Description/ 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.
Required Qualifications
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.
Required Technical Expertise
Supervised Learning
o Linear regression and logistic regression,
o Decision trees, Random Forest, Gradient Boosting (XGBoost, LightGBM, CatBoost),
o Support Vector Machines (SVMs) and kernel methods,
o Neural networks CNNs, RNNs, LSTMs, and Transformers,
o Classification, regression, and ranking problems,
o Cross-validation, bias-variance trade-off, regularization (L1/L2, dropout)
Unsupervised Learning
o Clustering: K-Means, DBSCAN, Gaussian Mixture Models, hierarchical clustering
o Dimensionality reduction: PCA, t-SNE, UMAP
o Autoencoders and variational autoencoders (VAEs)
o Anomaly detection and outlier identification
o Association rule mining (Apriori, FP-Growth)
o Topic modelling (LDA, NMF)
Reinforcement Learning
o Markov Decision Processes (MDPs) states, actions, rewards, transitions
o Model-free methods: Q-Learning, SARSA, Deep Q-Networks (DQN)
o Policy gradient methods: REINFORCE, PPO, A3C / A2C
o Actor-Critic architectures
o Multi-armed bandits and contextual bandits
o Reward shaping, environment design, and simulation frameworks (OpenAI Gym)
Relevant learning algorithms - Adjacent & advanced techniques
o Transfer learning and fine-tuning pre-trained models
o Semi-supervised and self-supervised learning
o Active learning and human-in-the-loop pipelines
o Federated learning for privacy-preserving training
o Bayesian optimization and hyperparameter tuning (Optuna, Ray Tune)
o Ensemble methods, stacking, and model blending
o Graph Neural Networks (GNNs) a plus
o Causal inference and counterfactual reasoning a plus
Good to Have
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.