FSE Sr.AI Engineer

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.